{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\LT-054912\surfdrive\Voting for niche parties\dataverse files\Stata commands to replicate all analyses.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 3 Oct 2023, 17:31:14
{txt}
{com}. 
. ************
. **Figure 2**
. ************
. 
. use "data for analysis.dta", clear
{txt}( )

{com}. 
. preserve
{txt}
{com}. 
. drop if c_niche>=. | male>=. | age>=. | highedu>=. | income_3cat>=. | dissatisfied>=. | closeparty>=. | distpreviouspartycmp>=. | p_government>=. | ///
> lvotetotniche_combined>=. | lpss_mod3_upd>=. | sd_rile>=. | p_niche>=. | country_elec>=.
{txt}(202,374 observations deleted)

{com}. egen zage = std(age)
{txt}
{com}. label var zage "Age"
{txt}
{com}. egen zdistpreviouspartycmp = std(distpreviouspartycmp)
{txt}
{com}. label var zdistpreviouspartycmp "Left/right distance party t-1"
{txt}
{com}. egen zsd_rile = std(sd_rile)
{txt}
{com}. label var zsd_rile "Polarization mainstream parties"
{txt}
{com}. egen zlvotetotniche_combined = std(lvotetotniche_combined)
{txt}
{com}. label var zlvotetotniche_combined "Vote share niche parties t-1"
{txt}
{com}. egen zlpss_mod3_upd = std(lpss_mod3_upd)
{txt}
{com}. label var zlpss_mod3_upd "Party system saturation t-1"
{txt}
{com}. melogit c_niche male zage i.highedu i.income_3cat dissatisfied zdistpreviouspartycmp closeparty p_government zsd_rile zlvotetotniche_combined zlpss_mod3_upd ///
> if p_niche==0 || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -7330.739}  
Iteration 1:{space 3}log likelihood = {res:-6961.3936}  
Iteration 2:{space 3}log likelihood = {res:-6959.7544}  
Iteration 3:{space 3}log likelihood = {res:-6959.7524}  
Iteration 4:{space 3}log likelihood = {res:-6959.7524}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6798.9628}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6798.9628}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6794.7802}  
Iteration 2:{space 3}log pseudolikelihood = {res: -6784.272}  
Iteration 3:{space 3}log pseudolikelihood = {res:-6773.1833}  
Iteration 4:{space 3}log pseudolikelihood = {res:-6773.1452}  
Iteration 5:{space 3}log pseudolikelihood = {res:-6773.1451}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,872
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}     663.4
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   444.02
{txt}Log pseudolikelihood = {res}-6773.1451{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.085267{col 39}{space 2}  .069409{col 50}{space 1}    1.28{col 59}{space 3}0.201{col 67}{space 4} .9574088{col 80}{space 3}   1.2302
{txt}{space 21}zage {c |}{col 27}{res}{space 2} .7835572{col 39}{space 2} .0316487{col 50}{space 1}   -6.04{col 59}{space 3}0.000{col 67}{space 4} .7239186{col 80}{space 3}  .848109
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.273162{col 39}{space 2} .1216711{col 50}{space 1}    2.53{col 59}{space 3}0.012{col 67}{space 4} 1.055693{col 80}{space 3} 1.535428
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  1.37776{col 39}{space 2} .1755047{col 50}{space 1}    2.52{col 59}{space 3}0.012{col 67}{space 4} 1.073357{col 80}{space 3} 1.768492
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8141882{col 39}{space 2} .0557074{col 50}{space 1}   -3.00{col 59}{space 3}0.003{col 67}{space 4}  .712008{col 80}{space 3} .9310322
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6456305{col 39}{space 2} .0596001{col 50}{space 1}   -4.74{col 59}{space 3}0.000{col 67}{space 4} .5387745{col 80}{space 3} .7736793
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.099625{col 39}{space 2} .1766009{col 50}{space 1}    8.82{col 59}{space 3}0.000{col 67}{space 4} 1.780519{col 80}{space 3} 2.475922
{txt}{space 4}zdistpreviouspartycmp {c |}{col 27}{res}{space 2} .9588442{col 39}{space 2} .0527954{col 50}{space 1}   -0.76{col 59}{space 3}0.445{col 67}{space 4} .8607551{col 80}{space 3} 1.068111
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3945243{col 39}{space 2} .0405786{col 50}{space 1}   -9.04{col 59}{space 3}0.000{col 67}{space 4} .3224956{col 80}{space 3} .4826404
{txt}{space 13}p_government {c |}{col 27}{res}{space 2}  1.21842{col 39}{space 2} .2412989{col 50}{space 1}    1.00{col 59}{space 3}0.319{col 67}{space 4} .8264626{col 80}{space 3} 1.796268
{txt}{space 17}zsd_rile {c |}{col 27}{res}{space 2} 1.146064{col 39}{space 2} .1105443{col 50}{space 1}    1.41{col 59}{space 3}0.158{col 67}{space 4} .9486494{col 80}{space 3} 1.384561
{txt}{space 2}zlvotetotniche_combined {c |}{col 27}{res}{space 2} 1.374713{col 39}{space 2} .1660838{col 50}{space 1}    2.63{col 59}{space 3}0.008{col 67}{space 4} 1.084864{col 80}{space 3} 1.742002
{txt}{space 11}zlpss_mod3_upd {c |}{col 27}{res}{space 2} 1.143083{col 39}{space 2}   .09245{col 50}{space 1}    1.65{col 59}{space 3}0.098{col 67}{space 4} .9755158{col 80}{space 3} 1.339433
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0731909{col 39}{space 2} .0165177{col 50}{space 1}  -11.59{col 59}{space 3}0.000{col 67}{space 4} .0470283{col 80}{space 3} .1139083
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4328258{col 39}{space 2} .1604755{col 67}{space 4} .2092761{col 80}{space 3} .8951722
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. coefplot, eform drop(_cons) xline(1) title("Mainstream to Niche") xlabel(0 (.5) 2) scheme(plotplain) name(figure2_a, replace)
{res}{txt}
{com}. melogit c_mainstream male zage i.highedu i.income_3cat dissatisfied zdistpreviouspartycmp closeparty p_government zsd_rile zlvotetotniche_combined zlpss_mod3_upd ///
> if p_niche==1 || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2261.6559}  
Iteration 1:{space 3}log likelihood = {res:-2256.0369}  
Iteration 2:{space 3}log likelihood = {res:-2256.0321}  
Iteration 3:{space 3}log likelihood = {res:-2256.0321}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2243.8894}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2243.8894}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2237.3775}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2235.9655}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2235.3714}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2235.3711}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2235.3711}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,515
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     141.1
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   227.54
{txt}Log pseudolikelihood = {res}-2235.3711{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:32} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8236391{col 39}{space 2} .0882955{col 50}{space 1}   -1.81{col 59}{space 3}0.070{col 67}{space 4} .6675545{col 80}{space 3} 1.016219
{txt}{space 21}zage {c |}{col 27}{res}{space 2} .8987916{col 39}{space 2} .0399785{col 50}{space 1}   -2.40{col 59}{space 3}0.016{col 67}{space 4} .8237536{col 80}{space 3} .9806651
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.298017{col 39}{space 2} .1592009{col 50}{space 1}    2.13{col 59}{space 3}0.033{col 67}{space 4} 1.020659{col 80}{space 3} 1.650743
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.039549{col 39}{space 2} .1456443{col 50}{space 1}    0.28{col 59}{space 3}0.782{col 67}{space 4} .7899303{col 80}{space 3} 1.368047
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.125471{col 39}{space 2} .1155771{col 50}{space 1}    1.15{col 59}{space 3}0.250{col 67}{space 4}  .920285{col 80}{space 3} 1.376404
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.612894{col 39}{space 2} .1886577{col 50}{space 1}    4.09{col 59}{space 3}0.000{col 67}{space 4} 1.282455{col 80}{space 3} 2.028475
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .7953483{col 39}{space 2} .0664123{col 50}{space 1}   -2.74{col 59}{space 3}0.006{col 67}{space 4} .6752758{col 80}{space 3}  .936771
{txt}{space 4}zdistpreviouspartycmp {c |}{col 27}{res}{space 2}  1.19195{col 39}{space 2} .0699206{col 50}{space 1}    2.99{col 59}{space 3}0.003{col 67}{space 4} 1.062493{col 80}{space 3} 1.337181
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4279681{col 39}{space 2} .0407272{col 50}{space 1}   -8.92{col 59}{space 3}0.000{col 67}{space 4} .3551466{col 80}{space 3} .5157214
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7525457{col 39}{space 2} .1463434{col 50}{space 1}   -1.46{col 59}{space 3}0.144{col 67}{space 4} .5140489{col 80}{space 3} 1.101695
{txt}{space 17}zsd_rile {c |}{col 27}{res}{space 2} .9317811{col 39}{space 2} .0921666{col 50}{space 1}   -0.71{col 59}{space 3}0.475{col 67}{space 4} .7675696{col 80}{space 3} 1.131123
{txt}{space 2}zlvotetotniche_combined {c |}{col 27}{res}{space 2} .9350405{col 39}{space 2} .0903957{col 50}{space 1}   -0.69{col 59}{space 3}0.487{col 67}{space 4} .7736419{col 80}{space 3} 1.130111
{txt}{space 11}zlpss_mod3_upd {c |}{col 27}{res}{space 2} .9116827{col 39}{space 2} .0433785{col 50}{space 1}   -1.94{col 59}{space 3}0.052{col 67}{space 4} .8305064{col 80}{space 3} 1.000793
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .4135771{col 39}{space 2} .0657844{col 50}{space 1}   -5.55{col 59}{space 3}0.000{col 67}{space 4} .3028048{col 80}{space 3} .5648723
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1920233{col 39}{space 2} .1404232{col 67}{space 4} .0458021{col 80}{space 3} .8050491
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. coefplot, eform drop(_cons) xline(1) title("Niche to Mainstream") xlabel(0 (.5) 2) scheme(plotplain) name(figure2_b, replace)
{res}{txt}
{com}. graph combine figure2_a figure2_b, scheme(plotplain)
{res}{txt}
{com}. graph export "figure2.tif", replace
{txt}{p 0 4 2}
file {bf}
figure2.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. restore
{txt}
{com}. 
. *************************
. **Figure 3 and Table A5**
. *************************
. 
. preserve
{txt}
{com}. 
. ***Figure 3
. 
. drop if c_niche>=. | male>=. | age>=. | highedu>=. | income_3cat>=. | dissatisfied>=. | closeparty>=. | distpreviouspartycmp>=. | p_government>=. | ///
> lvotetotniche_combined>=. | lpss_mod3_upd>=. | sd_rile>=. | p_niche>=. | country_elec>=.
{txt}(202,374 observations deleted)

{com}. egen zage = std(age)
{txt}
{com}. label var zage "Age"
{txt}
{com}. egen zdistpreviouspartycmp = std(distpreviouspartycmp)
{txt}
{com}. label var zdistpreviouspartycmp "Left/right distance party t-1"
{txt}
{com}. egen zsd_rile = std(sd_rile)
{txt}
{com}. label var zsd_rile "Polarization mainstream parties"
{txt}
{com}. egen zlvotetotniche_combined = std(lvotetotniche_combined)
{txt}
{com}. label var zlvotetotniche_combined "Vote share niche parties t-1"
{txt}
{com}. egen zlpss_mod3_upd = std(lpss_mod3_upd)
{txt}
{com}. label var zlpss_mod3_upd "Party system saturation t-1"
{txt}
{com}. egen zlvotetotradicallr_combined = std(lvotetotradicallr_combined)
{txt}
{com}. label var zlvotetotradicallr_combined "Vote share radical parties t-1"
{txt}
{com}. egen zlvotetotgreen_combined = std(lvotetotgreen_combined)
{txt}
{com}. label var zlvotetotgreen_combined "Vote share green parties t-1"
{txt}
{com}. melogit c_radicalrl_vs_mainstream male zage i.highedu dissatisfied i.income_3cat zdistpreviouspartycmp closeparty p_government zsd_rile zlvotetotradicallr_combined zlpss_mod3_upd ///
> if p_radicalrl_vs_mainstream==0 || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5446.0864}  
Iteration 1:{space 3}log likelihood = {res:-4784.8472}  
Iteration 2:{space 3}log likelihood = {res:-4772.3847}  
Iteration 3:{space 3}log likelihood = {res: -4772.316}  
Iteration 4:{space 3}log likelihood = {res: -4772.316}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4454.9943}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4454.9943}  
Iteration 1:{space 3}log pseudolikelihood = {res: -4429.056}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4418.9799}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4417.8913}  
Iteration 4:{space 3}log pseudolikelihood = {res: -4417.827}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4417.8266}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,042
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     642.1
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   850.93
{txt}Log pseudolikelihood = {res}-4417.8266{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 93:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}  c_radicalrl_vs_mainstream{col 29}{c |} Odds ratio{col 41}   std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}male {c |}{col 29}{res}{space 2} 1.314875{col 41}{space 2} .0917461{col 52}{space 1}    3.92{col 61}{space 3}0.000{col 69}{space 4} 1.146809{col 82}{space 3}  1.50757
{txt}{space 23}zage {c |}{col 29}{res}{space 2} .8222169{col 41}{space 2} .0393563{col 52}{space 1}   -4.09{col 61}{space 3}0.000{col 69}{space 4} .7485879{col 82}{space 3}  .903088
{txt}{space 27} {c |}
{space 20}highedu {c |}
{space 7}Secondary education  {c |}{col 29}{res}{space 2} 1.373475{col 41}{space 2} .1874012{col 52}{space 1}    2.33{col 61}{space 3}0.020{col 69}{space 4} 1.051187{col 82}{space 3} 1.794574
{txt}{space 2}Post-secondary education  {c |}{col 29}{res}{space 2} 1.159285{col 41}{space 2} .1866561{col 52}{space 1}    0.92{col 61}{space 3}0.359{col 69}{space 4} .8455482{col 82}{space 3} 1.589432
{txt}{space 27} {c |}
{space 15}dissatisfied {c |}{col 29}{res}{space 2} 2.705637{col 41}{space 2} .2618107{col 52}{space 1}   10.29{col 61}{space 3}0.000{col 69}{space 4} 2.238222{col 82}{space 3} 3.270664
{txt}{space 27} {c |}
{space 16}income_3cat {c |}
{space 13}Medium income  {c |}{col 29}{res}{space 2} .7410266{col 41}{space 2}  .060399{col 52}{space 1}   -3.68{col 61}{space 3}0.000{col 69}{space 4} .6316184{col 82}{space 3} .8693863
{txt}{space 15}High income  {c |}{col 29}{res}{space 2} .5434437{col 41}{space 2}  .052065{col 52}{space 1}   -6.37{col 61}{space 3}0.000{col 69}{space 4} .4504063{col 82}{space 3}  .655699
{txt}{space 27} {c |}
{space 6}zdistpreviouspartycmp {c |}{col 29}{res}{space 2} 1.059362{col 41}{space 2} .0675211{col 52}{space 1}    0.90{col 61}{space 3}0.366{col 69}{space 4} .9349555{col 82}{space 3} 1.200322
{txt}{space 17}closeparty {c |}{col 29}{res}{space 2} .3516655{col 41}{space 2}  .045744{col 52}{space 1}   -8.03{col 61}{space 3}0.000{col 69}{space 4} .2725254{col 82}{space 3} .4537874
{txt}{space 15}p_government {c |}{col 29}{res}{space 2} 1.160345{col 41}{space 2} .2664803{col 52}{space 1}    0.65{col 61}{space 3}0.517{col 69}{space 4} .7397813{col 82}{space 3} 1.819997
{txt}{space 19}zsd_rile {c |}{col 29}{res}{space 2} .7379548{col 41}{space 2}  .173687{col 52}{space 1}   -1.29{col 61}{space 3}0.197{col 69}{space 4} .4652525{col 82}{space 3} 1.170498
{txt}zlvotetotradicallr_combined {c |}{col 29}{res}{space 2} 2.228223{col 41}{space 2} .6938338{col 52}{space 1}    2.57{col 61}{space 3}0.010{col 69}{space 4} 1.210341{col 82}{space 3} 4.102131
{txt}{space 13}zlpss_mod3_upd {c |}{col 29}{res}{space 2} 1.911074{col 41}{space 2} .4176059{col 52}{space 1}    2.96{col 61}{space 3}0.003{col 69}{space 4}   1.2453{col 82}{space 3} 2.932792
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .0247422{col 41}{space 2} .0093402{col 52}{space 1}   -9.80{col 61}{space 3}0.000{col 69}{space 4} .0118062{col 82}{space 3} .0518521
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec               {col 29}{txt}{c |}
{space 18}var(_cons){c |}{col 29}{res}{space 2} 1.995225{col 41}{space 2} .8781744{col 69}{space 4} .8420649{col 82}{space 3} 4.727571
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. coefplot, eform drop(_cons) xline(1) title("Mainstream to Radical") xlabel(0 (1) 5) scheme(plotplain) name(figure4_1, replace)
{res}{txt}
{com}. melogit c_mainstream_vs_radicalrl zage zage i.highedu dissatisfied i.income_3cat zdistpreviouspartycmp closeparty p_government zsd_rile zlvotetotradicallr_combined zlpss_mod3_upd ///
> if p_radicalrl_vs_mainstream==1 || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1324.8968}  
Iteration 1:{space 3}log likelihood = {res:-1319.9079}  
Iteration 2:{space 3}log likelihood = {res: -1319.895}  
Iteration 3:{space 3}log likelihood = {res: -1319.895}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1293.2763}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1293.2763}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1290.8887}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1287.2079}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1287.1498}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1287.1495}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1287.1495}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,716
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     100.6
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   416.26
{txt}Log pseudolikelihood = {res}-1287.1495{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 93:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}  c_mainstream_vs_radicalrl{col 29}{c |} Odds ratio{col 41}   std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}zage {c |}{col 29}{res}{space 2} .8880053{col 41}{space 2} .0523862{col 52}{space 1}   -2.01{col 61}{space 3}0.044{col 69}{space 4} .7910438{col 82}{space 3} .9968517
{txt}{space 27} {c |}
{space 20}highedu {c |}
{space 7}Secondary education  {c |}{col 29}{res}{space 2} 1.163087{col 41}{space 2} .1991381{col 52}{space 1}    0.88{col 61}{space 3}0.378{col 69}{space 4} .8315214{col 82}{space 3} 1.626862
{txt}{space 2}Post-secondary education  {c |}{col 29}{res}{space 2} .7547072{col 41}{space 2} .1554986{col 52}{space 1}   -1.37{col 61}{space 3}0.172{col 69}{space 4} .5039626{col 82}{space 3} 1.130209
{txt}{space 27} {c |}
{space 15}dissatisfied {c |}{col 29}{res}{space 2} .6650693{col 41}{space 2} .0526264{col 52}{space 1}   -5.15{col 61}{space 3}0.000{col 69}{space 4} .5695239{col 82}{space 3} .7766437
{txt}{space 27} {c |}
{space 16}income_3cat {c |}
{space 13}Medium income  {c |}{col 29}{res}{space 2} 1.093887{col 41}{space 2} .1875762{col 52}{space 1}    0.52{col 61}{space 3}0.601{col 69}{space 4} .7816482{col 82}{space 3} 1.530854
{txt}{space 15}High income  {c |}{col 29}{res}{space 2} 1.744965{col 41}{space 2} .2811216{col 52}{space 1}    3.46{col 61}{space 3}0.001{col 69}{space 4}  1.27249{col 82}{space 3}  2.39287
{txt}{space 27} {c |}
{space 6}zdistpreviouspartycmp {c |}{col 29}{res}{space 2} 1.093952{col 41}{space 2} .0855679{col 52}{space 1}    1.15{col 61}{space 3}0.251{col 69}{space 4} .9384645{col 82}{space 3}   1.2752
{txt}{space 17}closeparty {c |}{col 29}{res}{space 2} .3917789{col 41}{space 2} .0429678{col 52}{space 1}   -8.54{col 61}{space 3}0.000{col 69}{space 4} .3159998{col 82}{space 3} .4857304
{txt}{space 15}p_government {c |}{col 29}{res}{space 2} 1.350796{col 41}{space 2} .4350615{col 52}{space 1}    0.93{col 61}{space 3}0.351{col 69}{space 4} .7185162{col 82}{space 3} 2.539469
{txt}{space 19}zsd_rile {c |}{col 29}{res}{space 2} .8034617{col 41}{space 2} .1371401{col 52}{space 1}   -1.28{col 61}{space 3}0.200{col 69}{space 4} .5750115{col 82}{space 3} 1.122674
{txt}zlvotetotradicallr_combined {c |}{col 29}{res}{space 2}  .853341{col 41}{space 2} .1425486{col 52}{space 1}   -0.95{col 61}{space 3}0.342{col 69}{space 4} .6150796{col 82}{space 3} 1.183897
{txt}{space 13}zlpss_mod3_upd {c |}{col 29}{res}{space 2} .9668928{col 41}{space 2} .1348689{col 52}{space 1}   -0.24{col 61}{space 3}0.809{col 69}{space 4} .7356087{col 82}{space 3} 1.270895
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .3823126{col 41}{space 2} .0866419{col 52}{space 1}   -4.24{col 61}{space 3}0.000{col 69}{space 4} .2451966{col 82}{space 3} .5961051
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec               {col 29}{txt}{c |}
{space 18}var(_cons){c |}{col 29}{res}{space 2} .4600894{col 41}{space 2}  .170351{col 69}{space 4}  .222679{col 82}{space 3} .9506158
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. coefplot, eform drop(_cons) xline(1) title("Radical to Mainstream") xlabel(0 (1) 5) scheme(plotplain) name(figure4_2, replace)
{res}{txt}
{com}. melogit c_green_vs_mainstream male zage i.highedu dissatisfied i.income_3cat zdistpreviouspartycmp closeparty p_government zsd_rile zlvotetotgreen_combined zlpss_mod3_upd ///
> if p_green_vs_mainstream==0 || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3539.2151}  
Iteration 1:{space 3}log likelihood = {res:-2399.5604}  
Iteration 2:{space 3}log likelihood = {res:-2321.8596}  
Iteration 3:{space 3}log likelihood = {res:-2315.0323}  
Iteration 4:{space 3}log likelihood = {res:-2314.9789}  
Iteration 5:{space 3}log likelihood = {res:-2314.9788}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2255.5089}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2255.5089}  
Iteration 1:{space 3}log pseudolikelihood = {res: -2248.813}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2245.1973}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2244.7353}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2244.7176}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2244.7175}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,275
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        70
{col 63}{txt}avg{col 67}={res}{col 69}     622.4
{col 63}{txt}max{col 67}={res}{col 69}     1,256

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   408.14
{txt}Log pseudolikelihood = {res}-2244.7175{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .679725{col 39}{space 2} .0504636{col 50}{space 1}   -5.20{col 59}{space 3}0.000{col 67}{space 4} .5876775{col 80}{space 3} .7861899
{txt}{space 21}zage {c |}{col 27}{res}{space 2} .7133109{col 39}{space 2} .0423897{col 50}{space 1}   -5.68{col 59}{space 3}0.000{col 67}{space 4} .6348846{col 80}{space 3} .8014251
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.741702{col 39}{space 2} .4729647{col 50}{space 1}    2.04{col 59}{space 3}0.041{col 67}{space 4} 1.022886{col 80}{space 3} 2.965653
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 3.320082{col 39}{space 2} 1.149194{col 50}{space 1}    3.47{col 59}{space 3}0.001{col 67}{space 4} 1.684686{col 80}{space 3} 6.543027
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.154091{col 39}{space 2} .1573893{col 50}{space 1}    1.05{col 59}{space 3}0.293{col 67}{space 4} .8833997{col 80}{space 3} 1.507727
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.041178{col 39}{space 2}  .138728{col 50}{space 1}    0.30{col 59}{space 3}0.762{col 67}{space 4} .8018802{col 80}{space 3} 1.351886
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9494744{col 39}{space 2} .1895929{col 50}{space 1}   -0.26{col 59}{space 3}0.795{col 67}{space 4} .6419684{col 80}{space 3} 1.404277
{txt}{space 25} {c |}
{space 4}zdistpreviouspartycmp {c |}{col 27}{res}{space 2} .8661349{col 39}{space 2} .0631425{col 50}{space 1}   -1.97{col 59}{space 3}0.049{col 67}{space 4} .7508129{col 80}{space 3} .9991699
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5369462{col 39}{space 2} .0561163{col 50}{space 1}   -5.95{col 59}{space 3}0.000{col 67}{space 4} .4374935{col 80}{space 3} .6590069
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.503588{col 39}{space 2}  .424747{col 50}{space 1}    1.44{col 59}{space 3}0.149{col 67}{space 4} .8643182{col 80}{space 3} 2.615675
{txt}{space 17}zsd_rile {c |}{col 27}{res}{space 2} .9409321{col 39}{space 2} .2795392{col 50}{space 1}   -0.20{col 59}{space 3}0.838{col 67}{space 4} .5256261{col 80}{space 3} 1.684378
{txt}{space 2}zlvotetotgreen_combined {c |}{col 27}{res}{space 2} 6.757606{col 39}{space 2} 2.325919{col 50}{space 1}    5.55{col 59}{space 3}0.000{col 67}{space 4} 3.442037{col 80}{space 3} 13.26692
{txt}{space 11}zlpss_mod3_upd {c |}{col 27}{res}{space 2} .9210657{col 39}{space 2} .2095634{col 50}{space 1}   -0.36{col 59}{space 3}0.718{col 67}{space 4} .5896888{col 80}{space 3} 1.438661
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0028148{col 39}{space 2} .0013414{col 50}{space 1}  -12.32{col 59}{space 3}0.000{col 67}{space 4} .0011062{col 80}{space 3} .0071628
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.830615{col 39}{space 2} .7776402{col 67}{space 4} .7961757{col 80}{space 3}  4.20906
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. coefplot, eform drop(_cons) xline(1) title("Mainstream to Green") xlabel(0 (1) 13) scheme(plotplain) name(figure4_3, replace)
{res}{txt}
{com}. melogit c_mainstream_vs_green male zage i.highedu dissatisfied i.income_3cat zdistpreviouspartycmp closeparty p_government zsd_rile zlvotetotgreen_combined zlpss_mod3_upd ///
> if p_green_vs_mainstream==1 || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-596.14212}  
Iteration 1:{space 3}log likelihood = {res:-595.45025}  
Iteration 2:{space 3}log likelihood = {res:-595.45005}  
Iteration 3:{space 3}log likelihood = {res:-595.45005}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-605.89047}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-605.89047}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-595.72194}  
Iteration 2:{space 3}log pseudolikelihood = {res:-595.58091}  
Iteration 3:{space 3}log pseudolikelihood = {res:-595.55806}  
Iteration 4:{space 3}log pseudolikelihood = {res:-595.53724}  
Iteration 5:{space 3}log pseudolikelihood = {res:-595.53709}  (backed up)
Iteration 6:{space 3}log pseudolikelihood = {res:-595.53707}  (backed up)
Iteration 7:{space 3}log pseudolikelihood = {res:-595.53707}  (backed up)
Iteration 8:{space 3}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 9:{space 3}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 16:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 18:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 25:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 33:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-595.53706}  (not concave)
Iteration 35:{space 2}log pseudolikelihood = {res:-595.53706}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-595.53705}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res:-595.53688}  (not concave)
Iteration 38:{space 2}log pseudolikelihood = {res:-595.53685}  
Iteration 39:{space 2}log pseudolikelihood = {res:-595.53617}  (backed up)
Iteration 40:{space 2}log pseudolikelihood = {res:-595.53484}  (not concave)
Iteration 41:{space 2}log pseudolikelihood = {res:-595.53481}  
Iteration 42:{space 2}log pseudolikelihood = {res: -595.5246}  (not concave)
Iteration 43:{space 2}log pseudolikelihood = {res:-595.52457}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res:-595.52381}  
Iteration 45:{space 2}log pseudolikelihood = {res:-595.49171}  
Iteration 46:{space 2}log pseudolikelihood = {res:-595.48198}  
Iteration 47:{space 2}log pseudolikelihood = {res:-595.45005}  
Iteration 48:{space 2}log pseudolikelihood = {res:-595.45005}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,045
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        21

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      49.8
{col 63}{txt}max{col 67}={res}{col 69}       177

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   133.77
{txt}Log pseudolikelihood = {res}-595.45005{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:21} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .793466{col 39}{space 2}  .102821{col 50}{space 1}   -1.79{col 59}{space 3}0.074{col 67}{space 4} .6154967{col 80}{space 3} 1.022895
{txt}{space 21}zage {c |}{col 27}{res}{space 2} 1.012279{col 39}{space 2} .1002552{col 50}{space 1}    0.12{col 59}{space 3}0.902{col 67}{space 4} .8336777{col 80}{space 3} 1.229143
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .9540056{col 39}{space 2} .2768859{col 50}{space 1}   -0.16{col 59}{space 3}0.871{col 67}{space 4}  .540135{col 80}{space 3} 1.684998
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8508612{col 39}{space 2} .2914845{col 50}{space 1}   -0.47{col 59}{space 3}0.637{col 67}{space 4} .4347681{col 80}{space 3} 1.665175
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.206687{col 39}{space 2} .2000077{col 50}{space 1}    1.13{col 59}{space 3}0.257{col 67}{space 4} .8719838{col 80}{space 3} 1.669863
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9538283{col 39}{space 2} .1262229{col 50}{space 1}   -0.36{col 59}{space 3}0.721{col 67}{space 4}  .735916{col 80}{space 3} 1.236267
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.003584{col 39}{space 2} .1834174{col 50}{space 1}    0.02{col 59}{space 3}0.984{col 67}{space 4} .7014331{col 80}{space 3} 1.435891
{txt}{space 25} {c |}
{space 4}zdistpreviouspartycmp {c |}{col 27}{res}{space 2} 1.433803{col 39}{space 2} .1408393{col 50}{space 1}    3.67{col 59}{space 3}0.000{col 67}{space 4} 1.182709{col 80}{space 3} 1.738205
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5579698{col 39}{space 2} .0974906{col 50}{space 1}   -3.34{col 59}{space 3}0.001{col 67}{space 4} .3961736{col 80}{space 3} .7858431
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .9346801{col 39}{space 2} .1695315{col 50}{space 1}   -0.37{col 59}{space 3}0.710{col 67}{space 4} .6550474{col 80}{space 3} 1.333685
{txt}{space 17}zsd_rile {c |}{col 27}{res}{space 2} 1.074748{col 39}{space 2} .0979853{col 50}{space 1}    0.79{col 59}{space 3}0.429{col 67}{space 4} .8988806{col 80}{space 3} 1.285023
{txt}{space 2}zlvotetotgreen_combined {c |}{col 27}{res}{space 2} .7702211{col 39}{space 2} .0961015{col 50}{space 1}   -2.09{col 59}{space 3}0.036{col 67}{space 4} .6031285{col 80}{space 3} .9836056
{txt}{space 11}zlpss_mod3_upd {c |}{col 27}{res}{space 2} 1.051276{col 39}{space 2} .0544293{col 50}{space 1}    0.97{col 59}{space 3}0.334{col 67}{space 4} .9498306{col 80}{space 3} 1.163556
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .8723029{col 39}{space 2} .2488028{col 50}{space 1}   -0.48{col 59}{space 3}0.632{col 67}{space 4} .4987502{col 80}{space 3} 1.525638
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 2.35e-35{col 39}{space 2} 2.45e-35{col 67}{space 4} 3.06e-36{col 80}{space 3} 1.81e-34
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. coefplot, eform drop(_cons) xline(1) title("Green to Mainstream") xlabel(0 (1) 5) scheme(plotplain) name(figure4_4, replace)
{res}{txt}
{com}. graph combine figure4_1 figure4_2 figure4_3 figure4_4, scheme(plotplain)
{res}{txt}
{com}. graph export "figure3.tif", replace
{txt}{p 0 4 2}
file {bf}
figure3.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. ***Table A5
. esttab M1 M2 M3 M4 M5 M6 using "tablea5.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A5. Standardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea5.rtf"'})

{com}. 
. restore
{txt}
{com}. 
. ************
. **Figure 4**
. ************
. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5446.0864}  
Iteration 1:{space 3}log likelihood = {res:-4784.8472}  
Iteration 2:{space 3}log likelihood = {res:-4772.3847}  
Iteration 3:{space 3}log likelihood = {res: -4772.316}  
Iteration 4:{space 3}log likelihood = {res: -4772.316}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4454.9943}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4454.9943}  
Iteration 1:{space 3}log pseudolikelihood = {res: -4429.056}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4418.9799}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4417.8913}  
Iteration 4:{space 3}log pseudolikelihood = {res: -4417.827}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4417.8266}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,042
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     642.1
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   850.93
{txt}Log pseudolikelihood = {res}-4417.8266{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.314875{col 40}{space 2} .0917461{col 51}{space 1}    3.92{col 60}{space 3}0.000{col 68}{space 4} 1.146809{col 81}{space 3}  1.50757
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9876143{col 40}{space 2} .0030098{col 51}{space 1}   -4.09{col 60}{space 3}0.000{col 68}{space 4} .9817328{col 81}{space 3}  .993531
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.373475{col 40}{space 2} .1874012{col 51}{space 1}    2.33{col 60}{space 3}0.020{col 68}{space 4} 1.051187{col 81}{space 3} 1.794574
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.159285{col 40}{space 2} .1866561{col 51}{space 1}    0.92{col 60}{space 3}0.359{col 68}{space 4} .8455482{col 81}{space 3} 1.589432
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.705637{col 40}{space 2} .2618107{col 51}{space 1}   10.29{col 60}{space 3}0.000{col 68}{space 4} 2.238222{col 81}{space 3} 3.270664
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7410266{col 40}{space 2}  .060399{col 51}{space 1}   -3.68{col 60}{space 3}0.000{col 68}{space 4} .6316184{col 81}{space 3} .8693863
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5434437{col 40}{space 2}  .052065{col 51}{space 1}   -6.37{col 60}{space 3}0.000{col 68}{space 4} .4504063{col 81}{space 3}  .655699
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.037372{col 40}{space 2} .0420689{col 51}{space 1}    0.90{col 60}{space 3}0.366{col 68}{space 4} .9581104{col 81}{space 3} 1.123191
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3516655{col 40}{space 2}  .045744{col 51}{space 1}   -8.03{col 60}{space 3}0.000{col 68}{space 4} .2725254{col 81}{space 3} .4537874
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.160345{col 40}{space 2} .2664803{col 51}{space 1}    0.65{col 60}{space 3}0.517{col 68}{space 4} .7397813{col 81}{space 3} 1.819997
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9642538{col 40}{space 2} .0271861{col 51}{space 1}   -1.29{col 60}{space 3}0.197{col 68}{space 4} .9124155{col 81}{space 3} 1.019037
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 1.087109{col 40}{space 2} .0352879{col 51}{space 1}    2.57{col 60}{space 3}0.010{col 68}{space 4}   1.0201{col 81}{space 3} 1.158519
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} 1.893088{col 40}{space 2} .4076357{col 51}{space 1}    2.96{col 60}{space 3}0.003{col 68}{space 4} 1.241317{col 81}{space 3} 2.887079
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0330472{col 40}{space 2} .0216054{col 51}{space 1}   -5.22{col 60}{space 3}0.000{col 68}{space 4} .0091758{col 81}{space 3}  .119022
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 1.995225{col 40}{space 2} .8781744{col 68}{space 4} .8420649{col 81}{space 3} 4.727571
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. sum lpss_mod3_upd if e(sample)==1, d

                 {txt}Party system saturation t-1
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.671103      -3.671103
{txt} 5%    {res} -2.56683      -3.671103
{txt}10%    {res}-.6910111      -3.671103       {txt}Obs         {res}     25,042
{txt}25%    {res}-.4533751      -3.671103       {txt}Sum of wgt. {res}     25,042

{txt}50%    {res} .0979672                      {txt}Mean          {res}-.0940574
                        {txt}Largest       Std. dev.     {res} 1.007249
{txt}75%    {res} .4480855       2.318012
{txt}90%    {res} .8802131       2.318012       {txt}Variance      {res} 1.014551
{txt}95%    {res} 1.294205       2.318012       {txt}Skewness      {res}-1.839774
{txt}99%    {res} 1.507976       2.318012       {txt}Kurtosis      {res} 7.388017
{txt}
{com}. margins, at(lpss_mod3_upd=(-4(0.5)2.5)) vsquish post
{res}
{txt}{col 1}Predictive margins{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:25,042}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-4}}
{lalign 8:2._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3.5}}
{lalign 8:3._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3}}
{lalign 8:4._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2.5}}
{lalign 8:5._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2}}
{lalign 8:6._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1.5}}
{lalign 8:7._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1}}
{lalign 8:8._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-.5}}
{lalign 8:9._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:0}}
{lalign 8:10._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:.5}}
{lalign 8:11._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1}}
{lalign 8:12._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1.5}}
{lalign 8:13._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2}}
{lalign 8:14._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2.5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0066962{col 26}{space 2} .0065563{col 37}{space 1}    1.02{col 46}{space 3}0.307{col 54}{space 4}-.0061538{col 67}{space 3} .0195463
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0089839{col 26}{space 2} .0077767{col 37}{space 1}    1.16{col 46}{space 3}0.248{col 54}{space 4}-.0062581{col 67}{space 3}  .024226
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0119924{col 26}{space 2} .0090401{col 37}{space 1}    1.33{col 46}{space 3}0.185{col 54}{space 4}-.0057259{col 67}{space 3} .0297107
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0159173{col 26}{space 2} .0102604{col 37}{space 1}    1.55{col 46}{space 3}0.121{col 54}{space 4}-.0041928{col 67}{space 3} .0360274
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0209924{col 26}{space 2} .0113185{col 37}{space 1}    1.85{col 46}{space 3}0.064{col 54}{space 4}-.0011914{col 67}{space 3} .0431763
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0274918{col 26}{space 2} .0120707{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4} .0038337{col 67}{space 3} .0511499
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0357283{col 26}{space 2} .0123844{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4} .0114553{col 67}{space 3} .0600012
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0460491{col 26}{space 2} .0122414{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} .0220565{col 67}{space 3} .0700418
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0588273{col 26}{space 2} .0120099{col 37}{space 1}    4.90{col 46}{space 3}0.000{col 54}{space 4} .0352882{col 67}{space 3} .0823663
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0744471{col 26}{space 2} .0129256{col 37}{space 1}    5.76{col 46}{space 3}0.000{col 54}{space 4} .0491134{col 67}{space 3} .0997808
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .0932849{col 26}{space 2} .0168826{col 37}{space 1}    5.53{col 46}{space 3}0.000{col 54}{space 4} .0601956{col 67}{space 3} .1263742
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1156848{col 26}{space 2}  .024833{col 37}{space 1}    4.66{col 46}{space 3}0.000{col 54}{space 4} .0670131{col 67}{space 3} .1643565
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .1419304{col 26}{space 2} .0366621{col 37}{space 1}    3.87{col 46}{space 3}0.000{col 54}{space 4} .0700741{col 67}{space 3} .2137868
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .1722155{col 26}{space 2} .0520829{col 37}{space 1}    3.31{col 46}{space 3}0.001{col 54}{space 4}  .070135{col 67}{space 3}  .274296
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, yline(0) ylabel(0(.1).5, nogrid angle(0)) plotregion(color(white)) graphregion(color(white))       ///
> xlabel(-4(0.5)2.5)                                                                                              ///
> recast(line) recastci(rline)                                                                    ///
> plot1opts(lcolor(black) clpattern(line) lwidth(medthick))                                               ///
> ci1opts (lcolor(black) clpattern(line) lpattern(dash) color(black) lwidth(medthick))                            ///
> xtitle("Party system saturation t-1") ytitle("Predicted probability of switching", margin(0 0 0 0)) title("Mainstream to Radical") nolab ///
> addplot(hist lpss_mod3_upd if e(sample)==1, percent bin(30) yaxis(2) ylabel(0(.1).5, nogrid angle(0)) xlabel(-4(0.5)2.5) lcolor(gs10) bfcolor(none) ///
> yscale(line alt axis(2)) ytitle("Percentage party system saturation", axis(2))) legend(off) scheme(plotplain) name(predicted1, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:lpss_mod3_upd}{p_end}
{p 0 4 2}
{txt}(note:  named style
line not found in class
linepattern,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
line not found in class
linepattern,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1323.8895}  
Iteration 1:{space 3}log likelihood = {res:-1318.8334}  
Iteration 2:{space 3}log likelihood = {res:-1318.8199}  
Iteration 3:{space 3}log likelihood = {res:-1318.8199}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1292.4674}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1292.4674}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1290.0443}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1286.5071}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1286.4579}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1286.4577}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,716
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     100.6
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   432.96
{txt}Log pseudolikelihood = {res}-1286.4577{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8875766{col 40}{space 2} .1334236{col 51}{space 1}   -0.79{col 60}{space 3}0.428{col 68}{space 4} .6610742{col 81}{space 3} 1.191685
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9927188{col 40}{space 2} .0037949{col 51}{space 1}   -1.91{col 60}{space 3}0.056{col 68}{space 4} .9853087{col 81}{space 3} 1.000185
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.155348{col 40}{space 2} .1986421{col 51}{space 1}    0.84{col 60}{space 3}0.401{col 68}{space 4} .8248279{col 81}{space 3} 1.618312
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7485508{col 40}{space 2} .1536245{col 51}{space 1}   -1.41{col 60}{space 3}0.158{col 68}{space 4} .5006449{col 81}{space 3} 1.119213
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .6637163{col 40}{space 2} .0520811{col 51}{space 1}   -5.22{col 60}{space 3}0.000{col 68}{space 4} .5691013{col 81}{space 3} .7740614
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.101644{col 40}{space 2} .1840659{col 51}{space 1}    0.58{col 60}{space 3}0.562{col 68}{space 4} .7939991{col 81}{space 3}  1.52849
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.761234{col 40}{space 2}  .280146{col 51}{space 1}    3.56{col 60}{space 3}0.000{col 68}{space 4} 1.289505{col 81}{space 3} 2.405532
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.058276{col 40}{space 2} .0526516{col 51}{space 1}    1.14{col 60}{space 3}0.255{col 68}{space 4}  .959953{col 81}{space 3} 1.166671
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3908702{col 40}{space 2} .0424524{col 51}{space 1}   -8.65{col 60}{space 3}0.000{col 68}{space 4} .3159248{col 81}{space 3} .4835946
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.321118{col 40}{space 2} .4252777{col 51}{space 1}    0.87{col 60}{space 3}0.387{col 68}{space 4} .7029651{col 81}{space 3} 2.482846
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9748289{col 40}{space 2} .0201706{col 51}{space 1}   -1.23{col 60}{space 3}0.218{col 68}{space 4} .9360862{col 81}{space 3} 1.015175
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9840084{col 40}{space 2} .0171463{col 51}{space 1}   -0.93{col 60}{space 3}0.355{col 68}{space 4} .9509697{col 81}{space 3} 1.018195
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} .9683809{col 40}{space 2}  .132668{col 51}{space 1}   -0.23{col 60}{space 3}0.815{col 68}{space 4} .7403409{col 81}{space 3} 1.266662
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .9786604{col 40}{space 2} .5073751{col 51}{space 1}   -0.04{col 60}{space 3}0.967{col 68}{space 4} .3542717{col 81}{space 3} 2.703508
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .4546731{col 40}{space 2} .1689967{col 68}{space 4} .2194409{col 81}{space 3} .9420651
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. margins, at(lpss_mod3_upd=(-4(0.5)2.5)) vsquish post
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:2,716}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-4}}
{lalign 8:2._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3.5}}
{lalign 8:3._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3}}
{lalign 8:4._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2.5}}
{lalign 8:5._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2}}
{lalign 8:6._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1.5}}
{lalign 8:7._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1}}
{lalign 8:8._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-.5}}
{lalign 8:9._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:0}}
{lalign 8:10._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:.5}}
{lalign 8:11._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1}}
{lalign 8:12._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1.5}}
{lalign 8:13._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2}}
{lalign 8:14._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2.5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2225925{col 26}{space 2} .0929506{col 37}{space 1}    2.39{col 46}{space 3}0.017{col 54}{space 4} .0404127{col 67}{space 3} .4047722
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2201762{col 26}{space 2} .0823433{col 37}{space 1}    2.67{col 46}{space 3}0.007{col 54}{space 4} .0587863{col 67}{space 3}  .381566
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2177769{col 26}{space 2} .0719691{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .0767202{col 67}{space 3} .3588337
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2153948{col 26}{space 2} .0618702{col 37}{space 1}    3.48{col 46}{space 3}0.000{col 54}{space 4} .0941314{col 67}{space 3} .3366582
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2130299{col 26}{space 2} .0521184{col 37}{space 1}    4.09{col 46}{space 3}0.000{col 54}{space 4} .1108796{col 67}{space 3} .3151802
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2106822{col 26}{space 2} .0428443{col 37}{space 1}    4.92{col 46}{space 3}0.000{col 54}{space 4}  .126709{col 67}{space 3} .2946554
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2083517{col 26}{space 2} .0343039{col 37}{space 1}    6.07{col 46}{space 3}0.000{col 54}{space 4} .1411174{col 67}{space 3} .2755861
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2060386{col 26}{space 2} .0270321{col 37}{space 1}    7.62{col 46}{space 3}0.000{col 54}{space 4} .1530566{col 67}{space 3} .2590205
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2037427{col 26}{space 2} .0221061{col 37}{space 1}    9.22{col 46}{space 3}0.000{col 54}{space 4} .1604155{col 67}{space 3}   .24707
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2014642{col 26}{space 2} .0210194{col 37}{space 1}    9.58{col 46}{space 3}0.000{col 54}{space 4} .1602669{col 67}{space 3} .2426615
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1992031{col 26}{space 2} .0241135{col 37}{space 1}    8.26{col 46}{space 3}0.000{col 54}{space 4} .1519415{col 67}{space 3} .2464647
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1969593{col 26}{space 2} .0299842{col 37}{space 1}    6.57{col 46}{space 3}0.000{col 54}{space 4} .1381913{col 67}{space 3} .2557273
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .1947329{col 26}{space 2}  .037231{col 37}{space 1}    5.23{col 46}{space 3}0.000{col 54}{space 4} .1217614{col 67}{space 3} .2677044
{txt}{space 9}14  {c |}{col 14}{res}{space 2}  .192524{col 26}{space 2} .0451046{col 37}{space 1}    4.27{col 46}{space 3}0.000{col 54}{space 4} .1041205{col 67}{space 3} .2809274
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, yline(0) ylabel(0(.1).5, nogrid angle(0)) plotregion(color(white)) graphregion(color(white))       ///
> xlabel(-4(0.5)2.5)                                                                                              ///
> recast(line) recastci(rline)                                                                    ///
> plot1opts(lcolor(black) clpattern(line) lwidth(medthick))                                               ///
> ci1opts (lcolor(black) clpattern(line) lpattern(dash) color(black) lwidth(medthick))                            ///
> xtitle("Party system saturation t-1") ytitle("Predicted probability of switching", margin(0 0 0 0)) title("Radical to Mainstream") nolab ///
> addplot(hist lpss_mod3_upd if e(sample)==1, percent bin(30) yaxis(2) ylabel(0(.1).5, nogrid angle(0)) xlabel(-4(0.5)2.5) lcolor(gs10) bfcolor(none) ///
> yscale(line alt axis(2)) ytitle("Percentage party system saturation", axis(2))) legend(off) scheme(plotplain) name(predicted2, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:lpss_mod3_upd}{p_end}
{p 0 4 2}
{txt}(note:  named style
line not found in class
linepattern,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
line not found in class
linepattern,  default attributes used)
{p_end}
{res}{txt}
{com}. graph combine predicted1 predicted2, scheme(plotplain)
{p 0 4 2}
{txt}(note:  named style
line not found in class
linepattern,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
line not found in class
linepattern,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
line not found in class
linepattern,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
line not found in class
linepattern,  default attributes used)
{p_end}
{res}{txt}
{com}. graph export "figure4.tif", replace
{txt}{p 0 4 2}
file {bf}
figure4.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. ************
. **Figure 6**
. ************
. 
. /*Based on coefficients in Table A21*/
. 
. mlogit c_radmainvsniche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> , baseoutcome(3) vce(cluster country_elec)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-19535.302}  
Iteration 1:{space 3}log pseudolikelihood = {res:-18751.168}  
Iteration 2:{space 3}log pseudolikelihood = {res: -18690.13}  
Iteration 3:{space 3}log pseudolikelihood = {res:-18689.846}  
Iteration 4:{space 3}log pseudolikelihood = {res:-18689.846}  
{res}
{txt}{col 1}Multinomial logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:24,990}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:2271.32}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-18689.846}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0433}

{txt}{ralign 94:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}            c_radmainvsniche{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      z{col 62}   P>|z|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Radical_party                {txt}{c |}
{space 24}male {c |}{col 30}{res}{space 2} .2817276{col 42}{space 2} .0673378{col 53}{space 1}    4.18{col 62}{space 3}0.000{col 70}{space 4} .1497479{col 83}{space 3} .4137073
{txt}{space 25}age {c |}{col 30}{res}{space 2}-.0060042{col 42}{space 2} .0034607{col 53}{space 1}   -1.73{col 62}{space 3}0.083{col 70}{space 4} -.012787{col 83}{space 3} .0007786
{txt}{space 28} {c |}
{space 21}highedu {c |}
{space 8}Secondary education  {c |}{col 30}{res}{space 2} 1.001534{col 42}{space 2} .2192038{col 53}{space 1}    4.57{col 62}{space 3}0.000{col 70}{space 4} .5719021{col 83}{space 3} 1.431165
{txt}{space 3}Post-secondary education  {c |}{col 30}{res}{space 2} 1.073359{col 42}{space 2} .2227588{col 53}{space 1}    4.82{col 62}{space 3}0.000{col 70}{space 4} .6367596{col 83}{space 3} 1.509958
{txt}{space 28} {c |}
{space 17}income_3cat {c |}
{space 14}Medium income  {c |}{col 30}{res}{space 2}-.3396915{col 42}{space 2} .0795146{col 53}{space 1}   -4.27{col 62}{space 3}0.000{col 70}{space 4}-.4955373{col 83}{space 3}-.1838457
{txt}{space 16}High income  {c |}{col 30}{res}{space 2}-.8844347{col 42}{space 2} .1537242{col 53}{space 1}   -5.75{col 62}{space 3}0.000{col 70}{space 4}-1.185729{col 83}{space 3}-.5831408
{txt}{space 28} {c |}
{space 16}dissatisfied {c |}{col 30}{res}{space 2} .8887338{col 42}{space 2}  .139291{col 53}{space 1}    6.38{col 62}{space 3}0.000{col 70}{space 4} .6157284{col 83}{space 3} 1.161739
{txt}{space 8}distpreviouspartycmp {c |}{col 30}{res}{space 2} .0117781{col 42}{space 2} .0421262{col 53}{space 1}    0.28{col 62}{space 3}0.780{col 70}{space 4}-.0707878{col 83}{space 3}  .094344
{txt}{space 18}closeparty {c |}{col 30}{res}{space 2}-.9826181{col 42}{space 2}  .150096{col 53}{space 1}   -6.55{col 62}{space 3}0.000{col 70}{space 4}-1.276801{col 83}{space 3}-.6884354
{txt}{space 16}p_government {c |}{col 30}{res}{space 2} .2272494{col 42}{space 2} .2326238{col 53}{space 1}    0.98{col 62}{space 3}0.329{col 70}{space 4}-.2286849{col 83}{space 3} .6831836
{txt}{space 21}sd_rile {c |}{col 30}{res}{space 2} .0001361{col 42}{space 2} .0184981{col 53}{space 1}    0.01{col 62}{space 3}0.994{col 70}{space 4}-.0361195{col 83}{space 3} .0363918
{txt}{space 6}lvotetotniche_combined {c |}{col 30}{res}{space 2} .0132563{col 42}{space 2} .0110996{col 53}{space 1}    1.19{col 62}{space 3}0.232{col 70}{space 4}-.0084986{col 83}{space 3} .0350112
{txt}{space 15}lpss_mod3_upd {c |}{col 30}{res}{space 2}  .309786{col 42}{space 2} .1962571{col 53}{space 1}    1.58{col 62}{space 3}0.114{col 70}{space 4}-.0748708{col 83}{space 3} .6944428
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-3.184252{col 42}{space 2} .5965803{col 53}{space 1}   -5.34{col 62}{space 3}0.000{col 70}{space 4}-4.353528{col 83}{space 3}-2.014976
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Radical_mainstream_party     {txt}{c |}
{space 24}male {c |}{col 30}{res}{space 2} .0549774{col 42}{space 2}  .047151{col 53}{space 1}    1.17{col 62}{space 3}0.244{col 70}{space 4}-.0374369{col 83}{space 3} .1473917
{txt}{space 25}age {c |}{col 30}{res}{space 2} .0021299{col 42}{space 2} .0029746{col 53}{space 1}    0.72{col 62}{space 3}0.474{col 70}{space 4}-.0037001{col 83}{space 3} .0079599
{txt}{space 28} {c |}
{space 21}highedu {c |}
{space 8}Secondary education  {c |}{col 30}{res}{space 2} .5004223{col 42}{space 2} .2059909{col 53}{space 1}    2.43{col 62}{space 3}0.015{col 70}{space 4} .0966876{col 83}{space 3} .9041571
{txt}{space 3}Post-secondary education  {c |}{col 30}{res}{space 2} .3746205{col 42}{space 2} .2342309{col 53}{space 1}    1.60{col 62}{space 3}0.110{col 70}{space 4}-.0844637{col 83}{space 3} .8337047
{txt}{space 28} {c |}
{space 17}income_3cat {c |}
{space 14}Medium income  {c |}{col 30}{res}{space 2}   .02465{col 42}{space 2} .0641251{col 53}{space 1}    0.38{col 62}{space 3}0.701{col 70}{space 4}-.1010329{col 83}{space 3} .1503329
{txt}{space 16}High income  {c |}{col 30}{res}{space 2}-.0769887{col 42}{space 2} .1173448{col 53}{space 1}   -0.66{col 62}{space 3}0.512{col 70}{space 4}-.3069802{col 83}{space 3} .1530029
{txt}{space 28} {c |}
{space 16}dissatisfied {c |}{col 30}{res}{space 2} .1816728{col 42}{space 2} .1662725{col 53}{space 1}    1.09{col 62}{space 3}0.275{col 70}{space 4}-.1442152{col 83}{space 3} .5075608
{txt}{space 8}distpreviouspartycmp {c |}{col 30}{res}{space 2} .0739518{col 42}{space 2} .0578423{col 53}{space 1}    1.28{col 62}{space 3}0.201{col 70}{space 4}-.0394169{col 83}{space 3} .1873206
{txt}{space 18}closeparty {c |}{col 30}{res}{space 2}  .019376{col 42}{space 2} .0909941{col 53}{space 1}    0.21{col 62}{space 3}0.831{col 70}{space 4}-.1589691{col 83}{space 3} .1977211
{txt}{space 16}p_government {c |}{col 30}{res}{space 2}  .118507{col 42}{space 2} .4631461{col 53}{space 1}    0.26{col 62}{space 3}0.798{col 70}{space 4}-.7892427{col 83}{space 3} 1.026257
{txt}{space 21}sd_rile {c |}{col 30}{res}{space 2} .0316836{col 42}{space 2} .0262398{col 53}{space 1}    1.21{col 62}{space 3}0.227{col 70}{space 4}-.0197454{col 83}{space 3} .0831126
{txt}{space 6}lvotetotniche_combined {c |}{col 30}{res}{space 2} .0054091{col 42}{space 2} .0167632{col 53}{space 1}    0.32{col 62}{space 3}0.747{col 70}{space 4}-.0274462{col 83}{space 3} .0382643
{txt}{space 15}lpss_mod3_upd {c |}{col 30}{res}{space 2}-.1386341{col 42}{space 2} .1605146{col 53}{space 1}   -0.86{col 62}{space 3}0.388{col 70}{space 4} -.453237{col 83}{space 3} .1759688
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-2.296387{col 42}{space 2} .6572383{col 53}{space 1}   -3.49{col 62}{space 3}0.000{col 70}{space 4} -3.58455{col 83}{space 3}-1.008223
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Non_radical_mainstream_party{col 30}{txt}{c |}  (base outcome)
{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. est store M1
{txt}
{com}. qui su lpss_mod3_upd if e(sample)==1, d
{txt}
{com}. margins, at(lpss_mod3_upd =(`r(p25)' `r(p75)')) predict(outcome(1)) pwcompare post
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:24,990}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(c_radmainvsniche==Radical_party), predict(outcome(1))}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 9:-.4533751}}
{lalign 7:2._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 9:.4480855}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method{col 37}         Una{col 51}djusted
{col 14}{c |}   Contrast{col 26}   std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 5}2 vs 1  {c |}{col 14}{res}{space 2} .0153822{col 26}{space 2} .0077452{col 37}{space 5} .0002019{col 51}{space 3} .0305626
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. gen b=el(e(b_vs),1,1) in 3
{txt}(232,760 missing values generated)

{com}. gen se=el(e(V_vs),1,1) in 3
{txt}(232,760 missing values generated)

{com}. 
. mlogit c_radmainvsniche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> , baseoutcome(3) vce(cluster country_elec)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-19535.302}  
Iteration 1:{space 3}log pseudolikelihood = {res:-18751.168}  
Iteration 2:{space 3}log pseudolikelihood = {res: -18690.13}  
Iteration 3:{space 3}log pseudolikelihood = {res:-18689.846}  
Iteration 4:{space 3}log pseudolikelihood = {res:-18689.846}  
{res}
{txt}{col 1}Multinomial logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:24,990}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:2271.32}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-18689.846}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0433}

{txt}{ralign 94:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}            c_radmainvsniche{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      z{col 62}   P>|z|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Radical_party                {txt}{c |}
{space 24}male {c |}{col 30}{res}{space 2} .2817276{col 42}{space 2} .0673378{col 53}{space 1}    4.18{col 62}{space 3}0.000{col 70}{space 4} .1497479{col 83}{space 3} .4137073
{txt}{space 25}age {c |}{col 30}{res}{space 2}-.0060042{col 42}{space 2} .0034607{col 53}{space 1}   -1.73{col 62}{space 3}0.083{col 70}{space 4} -.012787{col 83}{space 3} .0007786
{txt}{space 28} {c |}
{space 21}highedu {c |}
{space 8}Secondary education  {c |}{col 30}{res}{space 2} 1.001534{col 42}{space 2} .2192038{col 53}{space 1}    4.57{col 62}{space 3}0.000{col 70}{space 4} .5719021{col 83}{space 3} 1.431165
{txt}{space 3}Post-secondary education  {c |}{col 30}{res}{space 2} 1.073359{col 42}{space 2} .2227588{col 53}{space 1}    4.82{col 62}{space 3}0.000{col 70}{space 4} .6367596{col 83}{space 3} 1.509958
{txt}{space 28} {c |}
{space 17}income_3cat {c |}
{space 14}Medium income  {c |}{col 30}{res}{space 2}-.3396915{col 42}{space 2} .0795146{col 53}{space 1}   -4.27{col 62}{space 3}0.000{col 70}{space 4}-.4955373{col 83}{space 3}-.1838457
{txt}{space 16}High income  {c |}{col 30}{res}{space 2}-.8844347{col 42}{space 2} .1537242{col 53}{space 1}   -5.75{col 62}{space 3}0.000{col 70}{space 4}-1.185729{col 83}{space 3}-.5831408
{txt}{space 28} {c |}
{space 16}dissatisfied {c |}{col 30}{res}{space 2} .8887338{col 42}{space 2}  .139291{col 53}{space 1}    6.38{col 62}{space 3}0.000{col 70}{space 4} .6157284{col 83}{space 3} 1.161739
{txt}{space 8}distpreviouspartycmp {c |}{col 30}{res}{space 2} .0117781{col 42}{space 2} .0421262{col 53}{space 1}    0.28{col 62}{space 3}0.780{col 70}{space 4}-.0707878{col 83}{space 3}  .094344
{txt}{space 18}closeparty {c |}{col 30}{res}{space 2}-.9826181{col 42}{space 2}  .150096{col 53}{space 1}   -6.55{col 62}{space 3}0.000{col 70}{space 4}-1.276801{col 83}{space 3}-.6884354
{txt}{space 16}p_government {c |}{col 30}{res}{space 2} .2272494{col 42}{space 2} .2326238{col 53}{space 1}    0.98{col 62}{space 3}0.329{col 70}{space 4}-.2286849{col 83}{space 3} .6831836
{txt}{space 21}sd_rile {c |}{col 30}{res}{space 2} .0001361{col 42}{space 2} .0184981{col 53}{space 1}    0.01{col 62}{space 3}0.994{col 70}{space 4}-.0361195{col 83}{space 3} .0363918
{txt}{space 6}lvotetotniche_combined {c |}{col 30}{res}{space 2} .0132563{col 42}{space 2} .0110996{col 53}{space 1}    1.19{col 62}{space 3}0.232{col 70}{space 4}-.0084986{col 83}{space 3} .0350112
{txt}{space 15}lpss_mod3_upd {c |}{col 30}{res}{space 2}  .309786{col 42}{space 2} .1962571{col 53}{space 1}    1.58{col 62}{space 3}0.114{col 70}{space 4}-.0748708{col 83}{space 3} .6944428
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-3.184252{col 42}{space 2} .5965803{col 53}{space 1}   -5.34{col 62}{space 3}0.000{col 70}{space 4}-4.353528{col 83}{space 3}-2.014976
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Radical_mainstream_party     {txt}{c |}
{space 24}male {c |}{col 30}{res}{space 2} .0549774{col 42}{space 2}  .047151{col 53}{space 1}    1.17{col 62}{space 3}0.244{col 70}{space 4}-.0374369{col 83}{space 3} .1473917
{txt}{space 25}age {c |}{col 30}{res}{space 2} .0021299{col 42}{space 2} .0029746{col 53}{space 1}    0.72{col 62}{space 3}0.474{col 70}{space 4}-.0037001{col 83}{space 3} .0079599
{txt}{space 28} {c |}
{space 21}highedu {c |}
{space 8}Secondary education  {c |}{col 30}{res}{space 2} .5004223{col 42}{space 2} .2059909{col 53}{space 1}    2.43{col 62}{space 3}0.015{col 70}{space 4} .0966876{col 83}{space 3} .9041571
{txt}{space 3}Post-secondary education  {c |}{col 30}{res}{space 2} .3746205{col 42}{space 2} .2342309{col 53}{space 1}    1.60{col 62}{space 3}0.110{col 70}{space 4}-.0844637{col 83}{space 3} .8337047
{txt}{space 28} {c |}
{space 17}income_3cat {c |}
{space 14}Medium income  {c |}{col 30}{res}{space 2}   .02465{col 42}{space 2} .0641251{col 53}{space 1}    0.38{col 62}{space 3}0.701{col 70}{space 4}-.1010329{col 83}{space 3} .1503329
{txt}{space 16}High income  {c |}{col 30}{res}{space 2}-.0769887{col 42}{space 2} .1173448{col 53}{space 1}   -0.66{col 62}{space 3}0.512{col 70}{space 4}-.3069802{col 83}{space 3} .1530029
{txt}{space 28} {c |}
{space 16}dissatisfied {c |}{col 30}{res}{space 2} .1816728{col 42}{space 2} .1662725{col 53}{space 1}    1.09{col 62}{space 3}0.275{col 70}{space 4}-.1442152{col 83}{space 3} .5075608
{txt}{space 8}distpreviouspartycmp {c |}{col 30}{res}{space 2} .0739518{col 42}{space 2} .0578423{col 53}{space 1}    1.28{col 62}{space 3}0.201{col 70}{space 4}-.0394169{col 83}{space 3} .1873206
{txt}{space 18}closeparty {c |}{col 30}{res}{space 2}  .019376{col 42}{space 2} .0909941{col 53}{space 1}    0.21{col 62}{space 3}0.831{col 70}{space 4}-.1589691{col 83}{space 3} .1977211
{txt}{space 16}p_government {c |}{col 30}{res}{space 2}  .118507{col 42}{space 2} .4631461{col 53}{space 1}    0.26{col 62}{space 3}0.798{col 70}{space 4}-.7892427{col 83}{space 3} 1.026257
{txt}{space 21}sd_rile {c |}{col 30}{res}{space 2} .0316836{col 42}{space 2} .0262398{col 53}{space 1}    1.21{col 62}{space 3}0.227{col 70}{space 4}-.0197454{col 83}{space 3} .0831126
{txt}{space 6}lvotetotniche_combined {c |}{col 30}{res}{space 2} .0054091{col 42}{space 2} .0167632{col 53}{space 1}    0.32{col 62}{space 3}0.747{col 70}{space 4}-.0274462{col 83}{space 3} .0382643
{txt}{space 15}lpss_mod3_upd {c |}{col 30}{res}{space 2}-.1386341{col 42}{space 2} .1605146{col 53}{space 1}   -0.86{col 62}{space 3}0.388{col 70}{space 4} -.453237{col 83}{space 3} .1759688
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-2.296387{col 42}{space 2} .6572383{col 53}{space 1}   -3.49{col 62}{space 3}0.000{col 70}{space 4} -3.58455{col 83}{space 3}-1.008223
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Non_radical_mainstream_party{col 30}{txt}{c |}  (base outcome)
{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui su lpss_mod3_upd if e(sample)==1, d
{txt}
{com}. margins, at(lpss_mod3_upd =(`r(p25)' `r(p75)')) predict(outcome(2)) pwcompare post
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:24,990}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(c_radmainvsniche==Radical_mainstream_party), predict(outcome(2))}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 9:-.4533751}}
{lalign 7:2._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 9:.4480855}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method{col 37}         Una{col 51}djusted
{col 14}{c |}   Contrast{col 26}   std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 5}2 vs 1  {c |}{col 14}{res}{space 2}-.0282937{col 26}{space 2} .0272545{col 37}{space 5}-.0817115{col 51}{space 3} .0251242
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. replace b=el(e(b_vs),1,1) in 2
{txt}(1 real change made)

{com}. replace se=el(e(V_vs),1,1) in 2
{txt}(1 real change made)

{com}. 
. mlogit c_radmainvsniche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> , baseoutcome(3) vce(cluster country_elec)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-19535.302}  
Iteration 1:{space 3}log pseudolikelihood = {res:-18751.168}  
Iteration 2:{space 3}log pseudolikelihood = {res: -18690.13}  
Iteration 3:{space 3}log pseudolikelihood = {res:-18689.846}  
Iteration 4:{space 3}log pseudolikelihood = {res:-18689.846}  
{res}
{txt}{col 1}Multinomial logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:24,990}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:2271.32}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-18689.846}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0433}

{txt}{ralign 94:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}            c_radmainvsniche{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      z{col 62}   P>|z|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Radical_party                {txt}{c |}
{space 24}male {c |}{col 30}{res}{space 2} .2817276{col 42}{space 2} .0673378{col 53}{space 1}    4.18{col 62}{space 3}0.000{col 70}{space 4} .1497479{col 83}{space 3} .4137073
{txt}{space 25}age {c |}{col 30}{res}{space 2}-.0060042{col 42}{space 2} .0034607{col 53}{space 1}   -1.73{col 62}{space 3}0.083{col 70}{space 4} -.012787{col 83}{space 3} .0007786
{txt}{space 28} {c |}
{space 21}highedu {c |}
{space 8}Secondary education  {c |}{col 30}{res}{space 2} 1.001534{col 42}{space 2} .2192038{col 53}{space 1}    4.57{col 62}{space 3}0.000{col 70}{space 4} .5719021{col 83}{space 3} 1.431165
{txt}{space 3}Post-secondary education  {c |}{col 30}{res}{space 2} 1.073359{col 42}{space 2} .2227588{col 53}{space 1}    4.82{col 62}{space 3}0.000{col 70}{space 4} .6367596{col 83}{space 3} 1.509958
{txt}{space 28} {c |}
{space 17}income_3cat {c |}
{space 14}Medium income  {c |}{col 30}{res}{space 2}-.3396915{col 42}{space 2} .0795146{col 53}{space 1}   -4.27{col 62}{space 3}0.000{col 70}{space 4}-.4955373{col 83}{space 3}-.1838457
{txt}{space 16}High income  {c |}{col 30}{res}{space 2}-.8844347{col 42}{space 2} .1537242{col 53}{space 1}   -5.75{col 62}{space 3}0.000{col 70}{space 4}-1.185729{col 83}{space 3}-.5831408
{txt}{space 28} {c |}
{space 16}dissatisfied {c |}{col 30}{res}{space 2} .8887338{col 42}{space 2}  .139291{col 53}{space 1}    6.38{col 62}{space 3}0.000{col 70}{space 4} .6157284{col 83}{space 3} 1.161739
{txt}{space 8}distpreviouspartycmp {c |}{col 30}{res}{space 2} .0117781{col 42}{space 2} .0421262{col 53}{space 1}    0.28{col 62}{space 3}0.780{col 70}{space 4}-.0707878{col 83}{space 3}  .094344
{txt}{space 18}closeparty {c |}{col 30}{res}{space 2}-.9826181{col 42}{space 2}  .150096{col 53}{space 1}   -6.55{col 62}{space 3}0.000{col 70}{space 4}-1.276801{col 83}{space 3}-.6884354
{txt}{space 16}p_government {c |}{col 30}{res}{space 2} .2272494{col 42}{space 2} .2326238{col 53}{space 1}    0.98{col 62}{space 3}0.329{col 70}{space 4}-.2286849{col 83}{space 3} .6831836
{txt}{space 21}sd_rile {c |}{col 30}{res}{space 2} .0001361{col 42}{space 2} .0184981{col 53}{space 1}    0.01{col 62}{space 3}0.994{col 70}{space 4}-.0361195{col 83}{space 3} .0363918
{txt}{space 6}lvotetotniche_combined {c |}{col 30}{res}{space 2} .0132563{col 42}{space 2} .0110996{col 53}{space 1}    1.19{col 62}{space 3}0.232{col 70}{space 4}-.0084986{col 83}{space 3} .0350112
{txt}{space 15}lpss_mod3_upd {c |}{col 30}{res}{space 2}  .309786{col 42}{space 2} .1962571{col 53}{space 1}    1.58{col 62}{space 3}0.114{col 70}{space 4}-.0748708{col 83}{space 3} .6944428
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-3.184252{col 42}{space 2} .5965803{col 53}{space 1}   -5.34{col 62}{space 3}0.000{col 70}{space 4}-4.353528{col 83}{space 3}-2.014976
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Radical_mainstream_party     {txt}{c |}
{space 24}male {c |}{col 30}{res}{space 2} .0549774{col 42}{space 2}  .047151{col 53}{space 1}    1.17{col 62}{space 3}0.244{col 70}{space 4}-.0374369{col 83}{space 3} .1473917
{txt}{space 25}age {c |}{col 30}{res}{space 2} .0021299{col 42}{space 2} .0029746{col 53}{space 1}    0.72{col 62}{space 3}0.474{col 70}{space 4}-.0037001{col 83}{space 3} .0079599
{txt}{space 28} {c |}
{space 21}highedu {c |}
{space 8}Secondary education  {c |}{col 30}{res}{space 2} .5004223{col 42}{space 2} .2059909{col 53}{space 1}    2.43{col 62}{space 3}0.015{col 70}{space 4} .0966876{col 83}{space 3} .9041571
{txt}{space 3}Post-secondary education  {c |}{col 30}{res}{space 2} .3746205{col 42}{space 2} .2342309{col 53}{space 1}    1.60{col 62}{space 3}0.110{col 70}{space 4}-.0844637{col 83}{space 3} .8337047
{txt}{space 28} {c |}
{space 17}income_3cat {c |}
{space 14}Medium income  {c |}{col 30}{res}{space 2}   .02465{col 42}{space 2} .0641251{col 53}{space 1}    0.38{col 62}{space 3}0.701{col 70}{space 4}-.1010329{col 83}{space 3} .1503329
{txt}{space 16}High income  {c |}{col 30}{res}{space 2}-.0769887{col 42}{space 2} .1173448{col 53}{space 1}   -0.66{col 62}{space 3}0.512{col 70}{space 4}-.3069802{col 83}{space 3} .1530029
{txt}{space 28} {c |}
{space 16}dissatisfied {c |}{col 30}{res}{space 2} .1816728{col 42}{space 2} .1662725{col 53}{space 1}    1.09{col 62}{space 3}0.275{col 70}{space 4}-.1442152{col 83}{space 3} .5075608
{txt}{space 8}distpreviouspartycmp {c |}{col 30}{res}{space 2} .0739518{col 42}{space 2} .0578423{col 53}{space 1}    1.28{col 62}{space 3}0.201{col 70}{space 4}-.0394169{col 83}{space 3} .1873206
{txt}{space 18}closeparty {c |}{col 30}{res}{space 2}  .019376{col 42}{space 2} .0909941{col 53}{space 1}    0.21{col 62}{space 3}0.831{col 70}{space 4}-.1589691{col 83}{space 3} .1977211
{txt}{space 16}p_government {c |}{col 30}{res}{space 2}  .118507{col 42}{space 2} .4631461{col 53}{space 1}    0.26{col 62}{space 3}0.798{col 70}{space 4}-.7892427{col 83}{space 3} 1.026257
{txt}{space 21}sd_rile {c |}{col 30}{res}{space 2} .0316836{col 42}{space 2} .0262398{col 53}{space 1}    1.21{col 62}{space 3}0.227{col 70}{space 4}-.0197454{col 83}{space 3} .0831126
{txt}{space 6}lvotetotniche_combined {c |}{col 30}{res}{space 2} .0054091{col 42}{space 2} .0167632{col 53}{space 1}    0.32{col 62}{space 3}0.747{col 70}{space 4}-.0274462{col 83}{space 3} .0382643
{txt}{space 15}lpss_mod3_upd {c |}{col 30}{res}{space 2}-.1386341{col 42}{space 2} .1605146{col 53}{space 1}   -0.86{col 62}{space 3}0.388{col 70}{space 4} -.453237{col 83}{space 3} .1759688
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-2.296387{col 42}{space 2} .6572383{col 53}{space 1}   -3.49{col 62}{space 3}0.000{col 70}{space 4} -3.58455{col 83}{space 3}-1.008223
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Non_radical_mainstream_party{col 30}{txt}{c |}  (base outcome)
{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui su lpss_mod3_upd if e(sample)==1, d
{txt}
{com}. margins, at(lpss_mod3_upd =(`r(p25)' `r(p75)')) predict(outcome(3)) pwcompare post
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:24,990}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(c_radmainvsniche==Non_radical_mainstream_party), predict(outcome(3))}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 9:-.4533751}}
{lalign 7:2._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 9:.4480855}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method{col 37}         Una{col 51}djusted
{col 14}{c |}   Contrast{col 26}   std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 5}2 vs 1  {c |}{col 14}{res}{space 2} .0129114{col 26}{space 2}  .028704{col 37}{space 5}-.0433474{col 51}{space 3} .0691703
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. replace b=el(e(b_vs),1,1) in 1
{txt}(1 real change made)

{com}. replace se=el(e(V_vs),1,1) in 1
{txt}(1 real change made)

{com}. 
. gen lo=(b - 1.96*sqrt(se)) 
{txt}(232,758 missing values generated)

{com}. gen hi=(b + 1.96*sqrt(se)) 
{txt}(232,758 missing values generated)

{com}. 
. gen vertical=_n
{txt}
{com}. replace vertical=. if vertical>3
{txt}(232,758 real changes made, 232,758 to missing)

{com}. 
. twoway (scatter vertical b, msymbol(circle) mcolor(black)) ///
> (rcap lo hi vertical, lpattern(solid)lcolor(black) horizontal msize(zero)) ///
> , ylabel(3 "Radical right or left party" ///
> 2 "Radical mainstream party (+ 1 SD)" ///
> 1 "Non-radical mainstream party (< 1SD)" ///
> , angle(0) valuelabel ) ///
> xtitle(Increase in pred. probability) xline(0, lpattern(dash) lcolor(red)) ytitle("") xtitle(, size(medsmall)) title("") legend(off) ///
> scheme(plotplain) aspectratio(2) name(a, replace)
{res}{txt}
{com}. graph export "figure6.tif", replace
{txt}{p 0 4 2}
file {bf}
figure6.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. ************
. **Table A6**
. ************
. 
. /*Figure 4 is based on the coefficients of M3 and M4 below*/
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_niche==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -7330.739}  
Iteration 1:{space 3}log likelihood = {res:-6961.3936}  
Iteration 2:{space 3}log likelihood = {res:-6959.7544}  
Iteration 3:{space 3}log likelihood = {res:-6959.7524}  
Iteration 4:{space 3}log likelihood = {res:-6959.7524}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6798.9628}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6798.9628}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6794.7893}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6784.2505}  
Iteration 3:{space 3}log pseudolikelihood = {res:-6773.1835}  
Iteration 4:{space 3}log pseudolikelihood = {res:-6773.1452}  
Iteration 5:{space 3}log pseudolikelihood = {res:-6773.1451}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,872
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}     663.4
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   444.02
{txt}Log pseudolikelihood = {res}-6773.1451{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.085267{col 39}{space 2}  .069409{col 50}{space 1}    1.28{col 59}{space 3}0.201{col 67}{space 4} .9574088{col 80}{space 3}   1.2302
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9845907{col 39}{space 2}  .002532{col 50}{space 1}   -6.04{col 59}{space 3}0.000{col 67}{space 4} .9796406{col 80}{space 3} .9895658
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.273162{col 39}{space 2} .1216711{col 50}{space 1}    2.53{col 59}{space 3}0.012{col 67}{space 4} 1.055693{col 80}{space 3} 1.535428
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  1.37776{col 39}{space 2} .1755047{col 50}{space 1}    2.52{col 59}{space 3}0.012{col 67}{space 4} 1.073357{col 80}{space 3} 1.768492
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8141882{col 39}{space 2} .0557074{col 50}{space 1}   -3.00{col 59}{space 3}0.003{col 67}{space 4}  .712008{col 80}{space 3} .9310322
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6456305{col 39}{space 2} .0596001{col 50}{space 1}   -4.74{col 59}{space 3}0.000{col 67}{space 4} .5387745{col 80}{space 3} .7736793
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.099625{col 39}{space 2} .1766009{col 50}{space 1}    8.82{col 59}{space 3}0.000{col 67}{space 4} 1.780519{col 80}{space 3} 2.475922
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9736146{col 39}{space 2} .0341088{col 50}{space 1}   -0.76{col 59}{space 3}0.445{col 67}{space 4} .9090061{col 80}{space 3} 1.042815
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3945243{col 39}{space 2} .0405786{col 50}{space 1}   -9.04{col 59}{space 3}0.000{col 67}{space 4} .3224956{col 80}{space 3} .4826404
{txt}{space 13}p_government {c |}{col 27}{res}{space 2}  1.21842{col 39}{space 2} .2412989{col 50}{space 1}    1.00{col 59}{space 3}0.319{col 67}{space 4} .8264626{col 80}{space 3} 1.796268
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.016465{col 39}{space 2} .0117446{col 50}{space 1}    1.41{col 59}{space 3}0.158{col 67}{space 4} .9937051{col 80}{space 3} 1.039747
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2}  1.02665{col 39}{space 2} .0102506{col 50}{space 1}    2.63{col 59}{space 3}0.008{col 67}{space 4} 1.006755{col 80}{space 3} 1.046939
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.140853{col 39}{space 2} .0909225{col 50}{space 1}    1.65{col 59}{space 3}0.098{col 67}{space 4} .9758689{col 80}{space 3}  1.33373
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}  .078422{col 39}{space 2} .0304457{col 50}{space 1}   -6.56{col 59}{space 3}0.000{col 67}{space 4} .0366417{col 80}{space 3} .1678415
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4328263{col 39}{space 2} .1604758{col 67}{space 4} .2092762{col 80}{space 3} .8951739
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. gen mainniche2=e(sample)
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_niche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2261.6559}  
Iteration 1:{space 3}log likelihood = {res:-2256.0369}  
Iteration 2:{space 3}log likelihood = {res:-2256.0321}  
Iteration 3:{space 3}log likelihood = {res:-2256.0321}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2243.8894}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2243.8894}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2237.3936}  
Iteration 2:{space 3}log pseudolikelihood = {res:  -2235.99}  
Iteration 3:{space 3}log pseudolikelihood = {res: -2235.372}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2235.3711}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2235.3711}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,515
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     141.1
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   227.54
{txt}Log pseudolikelihood = {res}-2235.3711{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:32} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8236391{col 39}{space 2} .0882955{col 50}{space 1}   -1.81{col 59}{space 3}0.070{col 67}{space 4} .6675545{col 80}{space 3} 1.016219
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9932294{col 39}{space 2} .0028128{col 50}{space 1}   -2.40{col 59}{space 3}0.016{col 67}{space 4} .9877317{col 80}{space 3} .9987577
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.298017{col 39}{space 2} .1592009{col 50}{space 1}    2.13{col 59}{space 3}0.033{col 67}{space 4}  1.02066{col 80}{space 3} 1.650743
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.039549{col 39}{space 2} .1456443{col 50}{space 1}    0.28{col 59}{space 3}0.782{col 67}{space 4} .7899304{col 80}{space 3} 1.368047
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.125471{col 39}{space 2} .1155771{col 50}{space 1}    1.15{col 59}{space 3}0.250{col 67}{space 4}  .920285{col 80}{space 3} 1.376404
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.612894{col 39}{space 2} .1886577{col 50}{space 1}    4.09{col 59}{space 3}0.000{col 67}{space 4} 1.282455{col 80}{space 3} 2.028475
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .7953482{col 39}{space 2} .0664123{col 50}{space 1}   -2.74{col 59}{space 3}0.006{col 67}{space 4} .6752758{col 80}{space 3}  .936771
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2}   1.1182{col 39}{space 2} .0417348{col 50}{space 1}    2.99{col 59}{space 3}0.003{col 67}{space 4} 1.039322{col 80}{space 3} 1.203065
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4279681{col 39}{space 2} .0407272{col 50}{space 1}   -8.92{col 59}{space 3}0.000{col 67}{space 4} .3551466{col 80}{space 3} .5157214
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7525457{col 39}{space 2} .1463434{col 50}{space 1}   -1.46{col 59}{space 3}0.144{col 67}{space 4}  .514049{col 80}{space 3} 1.101695
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9915717{col 39}{space 2}  .011749{col 50}{space 1}   -0.71{col 59}{space 3}0.475{col 67}{space 4} .9688094{col 80}{space 3} 1.014869
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9944645{col 39}{space 2} .0079455{col 50}{space 1}   -0.69{col 59}{space 3}0.487{col 67}{space 4}  .979013{col 80}{space 3}  1.01016
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .9129143{col 39}{space 2} .0428029{col 50}{space 1}   -1.94{col 59}{space 3}0.052{col 67}{space 4} .8327614{col 80}{space 3} 1.000782
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}    .5944{col 39}{space 2} .2494334{col 50}{space 1}   -1.24{col 59}{space 3}0.215{col 67}{space 4} .2611449{col 80}{space 3} 1.352932
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1920232{col 39}{space 2} .1404231{col 67}{space 4} .0458021{col 80}{space 3} .8050484
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. gen nichemain2=e(sample)
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}32
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5446.0864}  
Iteration 1:{space 3}log likelihood = {res:-4784.8472}  
Iteration 2:{space 3}log likelihood = {res:-4772.3847}  
Iteration 3:{space 3}log likelihood = {res: -4772.316}  
Iteration 4:{space 3}log likelihood = {res: -4772.316}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4454.9943}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4454.9943}  
Iteration 1:{space 3}log pseudolikelihood = {res: -4429.056}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4418.9799}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4417.8913}  
Iteration 4:{space 3}log pseudolikelihood = {res: -4417.827}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4417.8266}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,042
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     642.1
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   850.93
{txt}Log pseudolikelihood = {res}-4417.8266{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.314875{col 40}{space 2} .0917461{col 51}{space 1}    3.92{col 60}{space 3}0.000{col 68}{space 4} 1.146809{col 81}{space 3}  1.50757
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9876143{col 40}{space 2} .0030098{col 51}{space 1}   -4.09{col 60}{space 3}0.000{col 68}{space 4} .9817328{col 81}{space 3}  .993531
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.373475{col 40}{space 2} .1874012{col 51}{space 1}    2.33{col 60}{space 3}0.020{col 68}{space 4} 1.051187{col 81}{space 3} 1.794574
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.159285{col 40}{space 2} .1866561{col 51}{space 1}    0.92{col 60}{space 3}0.359{col 68}{space 4} .8455482{col 81}{space 3} 1.589432
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.705637{col 40}{space 2} .2618107{col 51}{space 1}   10.29{col 60}{space 3}0.000{col 68}{space 4} 2.238222{col 81}{space 3} 3.270664
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7410266{col 40}{space 2}  .060399{col 51}{space 1}   -3.68{col 60}{space 3}0.000{col 68}{space 4} .6316184{col 81}{space 3} .8693863
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5434437{col 40}{space 2}  .052065{col 51}{space 1}   -6.37{col 60}{space 3}0.000{col 68}{space 4} .4504063{col 81}{space 3}  .655699
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.037372{col 40}{space 2} .0420689{col 51}{space 1}    0.90{col 60}{space 3}0.366{col 68}{space 4} .9581104{col 81}{space 3} 1.123191
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3516655{col 40}{space 2}  .045744{col 51}{space 1}   -8.03{col 60}{space 3}0.000{col 68}{space 4} .2725254{col 81}{space 3} .4537874
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.160345{col 40}{space 2} .2664803{col 51}{space 1}    0.65{col 60}{space 3}0.517{col 68}{space 4} .7397813{col 81}{space 3} 1.819997
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9642538{col 40}{space 2} .0271861{col 51}{space 1}   -1.29{col 60}{space 3}0.197{col 68}{space 4} .9124155{col 81}{space 3} 1.019037
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 1.087109{col 40}{space 2} .0352879{col 51}{space 1}    2.57{col 60}{space 3}0.010{col 68}{space 4}   1.0201{col 81}{space 3} 1.158519
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} 1.893088{col 40}{space 2} .4076357{col 51}{space 1}    2.96{col 60}{space 3}0.003{col 68}{space 4} 1.241317{col 81}{space 3} 2.887079
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0330472{col 40}{space 2} .0216054{col 51}{space 1}   -5.22{col 60}{space 3}0.000{col 68}{space 4} .0091758{col 81}{space 3}  .119022
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 1.995225{col 40}{space 2} .8781744{col 68}{space 4} .8420649{col 81}{space 3} 4.727571
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen mainradical2=e(sample)
{txt}
{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1323.8895}  
Iteration 1:{space 3}log likelihood = {res:-1318.8334}  
Iteration 2:{space 3}log likelihood = {res:-1318.8199}  
Iteration 3:{space 3}log likelihood = {res:-1318.8199}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1292.4674}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1292.4674}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1290.0443}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1286.5071}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1286.4579}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1286.4577}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,716
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     100.6
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   432.96
{txt}Log pseudolikelihood = {res}-1286.4577{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8875766{col 40}{space 2} .1334236{col 51}{space 1}   -0.79{col 60}{space 3}0.428{col 68}{space 4} .6610742{col 81}{space 3} 1.191685
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9927188{col 40}{space 2} .0037949{col 51}{space 1}   -1.91{col 60}{space 3}0.056{col 68}{space 4} .9853087{col 81}{space 3} 1.000185
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.155348{col 40}{space 2} .1986421{col 51}{space 1}    0.84{col 60}{space 3}0.401{col 68}{space 4} .8248279{col 81}{space 3} 1.618312
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7485508{col 40}{space 2} .1536245{col 51}{space 1}   -1.41{col 60}{space 3}0.158{col 68}{space 4} .5006449{col 81}{space 3} 1.119213
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .6637163{col 40}{space 2} .0520811{col 51}{space 1}   -5.22{col 60}{space 3}0.000{col 68}{space 4} .5691013{col 81}{space 3} .7740614
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.101644{col 40}{space 2} .1840659{col 51}{space 1}    0.58{col 60}{space 3}0.562{col 68}{space 4} .7939991{col 81}{space 3}  1.52849
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.761234{col 40}{space 2}  .280146{col 51}{space 1}    3.56{col 60}{space 3}0.000{col 68}{space 4} 1.289505{col 81}{space 3} 2.405532
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.058276{col 40}{space 2} .0526516{col 51}{space 1}    1.14{col 60}{space 3}0.255{col 68}{space 4}  .959953{col 81}{space 3} 1.166671
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3908702{col 40}{space 2} .0424524{col 51}{space 1}   -8.65{col 60}{space 3}0.000{col 68}{space 4} .3159248{col 81}{space 3} .4835946
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.321118{col 40}{space 2} .4252777{col 51}{space 1}    0.87{col 60}{space 3}0.387{col 68}{space 4} .7029651{col 81}{space 3} 2.482846
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9748289{col 40}{space 2} .0201706{col 51}{space 1}   -1.23{col 60}{space 3}0.218{col 68}{space 4} .9360862{col 81}{space 3} 1.015175
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9840084{col 40}{space 2} .0171463{col 51}{space 1}   -0.93{col 60}{space 3}0.355{col 68}{space 4} .9509697{col 81}{space 3} 1.018195
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} .9683809{col 40}{space 2}  .132668{col 51}{space 1}   -0.23{col 60}{space 3}0.815{col 68}{space 4} .7403409{col 81}{space 3} 1.266662
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .9786604{col 40}{space 2} .5073751{col 51}{space 1}   -0.04{col 60}{space 3}0.967{col 68}{space 4} .3542717{col 81}{space 3} 2.703508
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .4546731{col 40}{space 2} .1689967{col 68}{space 4} .2194409{col 81}{space 3} .9420651
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen radicalmain2=e(sample)
{txt}
{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}27
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3539.2151}  
Iteration 1:{space 3}log likelihood = {res:-2399.5604}  
Iteration 2:{space 3}log likelihood = {res:-2321.8596}  
Iteration 3:{space 3}log likelihood = {res:-2315.0323}  
Iteration 4:{space 3}log likelihood = {res:-2314.9789}  
Iteration 5:{space 3}log likelihood = {res:-2314.9788}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2255.5089}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2255.5089}  
Iteration 1:{space 3}log pseudolikelihood = {res: -2248.813}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2245.1973}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2244.7353}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2244.7176}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2244.7175}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,275
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        70
{col 63}{txt}avg{col 67}={res}{col 69}     622.4
{col 63}{txt}max{col 67}={res}{col 69}     1,256

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   408.14
{txt}Log pseudolikelihood = {res}-2244.7175{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .679725{col 39}{space 2} .0504636{col 50}{space 1}   -5.20{col 59}{space 3}0.000{col 67}{space 4} .5876775{col 80}{space 3} .7861899
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9787203{col 39}{space 2}  .003703{col 50}{space 1}   -5.68{col 59}{space 3}0.000{col 67}{space 4} .9714893{col 80}{space 3} .9860051
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.741702{col 39}{space 2} .4729647{col 50}{space 1}    2.04{col 59}{space 3}0.041{col 67}{space 4} 1.022886{col 80}{space 3} 2.965653
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 3.320082{col 39}{space 2} 1.149194{col 50}{space 1}    3.47{col 59}{space 3}0.001{col 67}{space 4} 1.684686{col 80}{space 3} 6.543027
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.154091{col 39}{space 2} .1573893{col 50}{space 1}    1.05{col 59}{space 3}0.293{col 67}{space 4} .8833997{col 80}{space 3} 1.507727
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.041178{col 39}{space 2}  .138728{col 50}{space 1}    0.30{col 59}{space 3}0.762{col 67}{space 4} .8018802{col 80}{space 3} 1.351886
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9494744{col 39}{space 2} .1895929{col 50}{space 1}   -0.26{col 59}{space 3}0.795{col 67}{space 4} .6419684{col 80}{space 3} 1.404277
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9126168{col 39}{space 2} .0423307{col 50}{space 1}   -1.97{col 59}{space 3}0.049{col 67}{space 4} .8333096{col 80}{space 3} .9994717
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5369462{col 39}{space 2} .0561163{col 50}{space 1}   -5.95{col 59}{space 3}0.000{col 67}{space 4} .4374935{col 80}{space 3} .6590069
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.503588{col 39}{space 2}  .424747{col 50}{space 1}    1.44{col 59}{space 3}0.149{col 67}{space 4} .8643182{col 80}{space 3} 2.615675
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9927332{col 39}{space 2} .0353293{col 50}{space 1}   -0.20{col 59}{space 3}0.838{col 67}{space 4} .9258488{col 80}{space 3} 1.064449
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.720218{col 39}{space 2} .1680972{col 50}{space 1}    5.55{col 59}{space 3}0.000{col 67}{space 4} 1.420383{col 80}{space 3} 2.083348
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .9221722{col 39}{space 2} .2067518{col 50}{space 1}   -0.36{col 59}{space 3}0.718{col 67}{space 4} .5942537{col 80}{space 3} 1.431041
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0013234{col 39}{space 2} .0012908{col 50}{space 1}   -6.79{col 59}{space 3}0.000{col 67}{space 4} .0001956{col 80}{space 3} .0089527
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.830615{col 39}{space 2} .7776402{col 67}{space 4} .7961757{col 80}{space 3}  4.20906
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. gen maingreen2=e(sample)
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-596.14212}  
Iteration 1:{space 3}log likelihood = {res:-595.45025}  
Iteration 2:{space 3}log likelihood = {res:-595.45005}  
Iteration 3:{space 3}log likelihood = {res:-595.45005}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-605.89047}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-605.89047}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-595.70217}  
Iteration 2:{space 3}log pseudolikelihood = {res:-595.64165}  
Iteration 3:{space 3}log pseudolikelihood = {res:-595.61595}  
Iteration 4:{space 3}log pseudolikelihood = {res:-595.61557}  (backed up)
Iteration 5:{space 3}log pseudolikelihood = {res:-595.61539}  (backed up)
Iteration 6:{space 3}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 7:{space 3}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 8:{space 3}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 9:{space 3}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 16:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 18:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 19:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 22:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 27:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 29:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 31:{space 2}log pseudolikelihood = {res:-595.61536}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 33:{space 2}log pseudolikelihood = {res:-595.61536}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-595.61534}  
Iteration 35:{space 2}log pseudolikelihood = {res:-595.61518}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-595.61487}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res:-595.61455}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-595.61423}  (backed up)
Iteration 39:{space 2}log pseudolikelihood = {res:-595.61391}  (not concave)
Iteration 40:{space 2}log pseudolikelihood = {res:-595.61366}  (not concave)
Iteration 41:{space 2}log pseudolikelihood = {res:-595.61284}  
Iteration 42:{space 2}log pseudolikelihood = {res:-595.60291}  
Iteration 43:{space 2}log pseudolikelihood = {res:-595.59821}  (backed up)
Iteration 44:{space 2}log pseudolikelihood = {res:-595.45008}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res:-595.45008}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-595.45008}  
Iteration 47:{space 2}log pseudolikelihood = {res:-595.45005}  (not concave)
Iteration 48:{space 2}log pseudolikelihood = {res:-595.45005}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-595.45005}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,045
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        21

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      49.8
{col 63}{txt}max{col 67}={res}{col 69}       177

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   133.77
{txt}Log pseudolikelihood = {res}-595.45005{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:21} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .793466{col 39}{space 2}  .102821{col 50}{space 1}   -1.79{col 59}{space 3}0.074{col 67}{space 4} .6154967{col 80}{space 3} 1.022895
{txt}{space 22}age {c |}{col 27}{res}{space 2} 1.000777{col 39}{space 2} .0063105{col 50}{space 1}    0.12{col 59}{space 3}0.902{col 67}{space 4} .9884851{col 80}{space 3} 1.013222
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .9540056{col 39}{space 2}  .276886{col 50}{space 1}   -0.16{col 59}{space 3}0.871{col 67}{space 4}  .540135{col 80}{space 3} 1.684999
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8508612{col 39}{space 2} .2914845{col 50}{space 1}   -0.47{col 59}{space 3}0.637{col 67}{space 4} .4347681{col 80}{space 3} 1.665175
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.206687{col 39}{space 2} .2000077{col 50}{space 1}    1.13{col 59}{space 3}0.257{col 67}{space 4} .8719838{col 80}{space 3} 1.669863
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9538283{col 39}{space 2} .1262229{col 50}{space 1}   -0.36{col 59}{space 3}0.721{col 67}{space 4}  .735916{col 80}{space 3} 1.236267
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.003584{col 39}{space 2} .1834174{col 50}{space 1}    0.02{col 59}{space 3}0.984{col 67}{space 4} .7014331{col 80}{space 3} 1.435891
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.257672{col 39}{space 2} .0786019{col 50}{space 1}    3.67{col 59}{space 3}0.000{col 67}{space 4} 1.112676{col 80}{space 3} 1.421561
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5579698{col 39}{space 2} .0974906{col 50}{space 1}   -3.34{col 59}{space 3}0.001{col 67}{space 4} .3961736{col 80}{space 3} .7858431
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .9346803{col 39}{space 2} .1695316{col 50}{space 1}   -0.37{col 59}{space 3}0.710{col 67}{space 4} .6550475{col 80}{space 3} 1.333685
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.008672{col 39}{space 2}  .011016{col 50}{space 1}    0.79{col 59}{space 3}0.429{col 67}{space 4}  .987311{col 80}{space 3} 1.030496
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9285587{col 39}{space 2} .0328927{col 50}{space 1}   -2.09{col 59}{space 3}0.036{col 67}{space 4} .8662773{col 80}{space 3} .9953179
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.050509{col 39}{space 2} .0535954{col 50}{space 1}    0.97{col 59}{space 3}0.334{col 67}{space 4} .9505446{col 80}{space 3} 1.160985
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .6125599{col 39}{space 2} .2508309{col 50}{space 1}   -1.20{col 59}{space 3}0.231{col 67}{space 4} .2745357{col 80}{space 3} 1.366779
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.63e-34{col 39}{space 2} 1.58e-33{col 67}{space 4} 9.40e-43{col 80}{space 3} 2.83e-26
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen greenmain2=e(sample)
{txt}
{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}21
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea6.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A6. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea6.rtf"'})

{com}. 
. ***************
. ***Table A4***
. ***************
. 
. preserve
{txt}
{com}. 
. keep if (mainniche2==1 | nichemain2==1)
{txt}(202,374 observations deleted)

{com}. 
. tab highedu, gen(edudum)

               {txt}Education {c |}      Freq.     Percent        Cum.
{hline 25}{c +}{hline 35}
       Primary education {c |}{res}      6,004       19.76       19.76
{txt}     Secondary education {c |}{res}     11,292       37.16       56.92
{txt}Post-secondary education {c |}{res}     13,091       43.08      100.00
{txt}{hline 25}{c +}{hline 35}
                   Total {c |}{res}     30,387      100.00
{txt}
{com}. tab income_3cat, gen(incdum)

       {txt}Income {c |}      Freq.     Percent        Cum.
{hline 14}{c +}{hline 35}
   Low income {c |}{res}     10,438       34.35       34.35
{txt}Medium income {c |}{res}     12,675       41.71       76.06
{txt}  High income {c |}{res}      7,274       23.94      100.00
{txt}{hline 14}{c +}{hline 35}
        Total {c |}{res}     30,387      100.00
{txt}
{com}. 
. capture drop mainniche nichemain mainradical radicalmain maingreen greenmain
{txt}
{com}. 
. gen mainniche=0
{txt}
{com}. replace mainniche=1 if c_niche==1 & p_niche==0
{txt}(2,171 real changes made)

{com}. 
. gen nichemain=0
{txt}
{com}. replace nichemain=1 if c_mainstream==1 & p_niche==1
{txt}(989 real changes made)

{com}. 
. gen mainradical=0
{txt}
{com}. replace mainradical=1 if c_radicalrl_vs_mainstream==1 & p_radicalrl_vs_mainstream==0
{txt}(1,341 real changes made)

{com}. 
. gen radicalmain=0
{txt}
{com}. replace radicalmain=1 if c_mainstream_vs_radicalrl==1 & p_radicalrl_vs_mainstream==1
{txt}(583 real changes made)

{com}.  
. gen maingreen=0
{txt}
{com}. replace maingreen=1 if c_green_vs_mainstream==1 & p_green_vs_mainstream==0
{txt}(574 real changes made)

{com}. 
. gen greenmain=0
{txt}
{com}. replace greenmain=1 if c_mainstream_vs_green==1 & p_green_vs_mainstream==1
{txt}(293 real changes made)

{com}. 
. label var mainniche "Mainstream to niche"
{txt}
{com}. label var nichemain "Niche to mainstream"
{txt}
{com}. label var mainradical "Mainstream to radical"
{txt}
{com}. label var radicalmain "Radical to mainstream"
{txt}
{com}. label var maingreen "Mainstream to green"
{txt}
{com}. label var greenmain "Green to mainstream"
{txt}
{com}. 
. keep mainniche nichemain mainradical radicalmain maingreen greenmain incdum* edudum* male dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd 
{txt}
{com}. 
. asdoc sum, save(tablea4.rtf) label
{txt}(File tablea4.rtf already exists, option {bf:append} was assumed)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}male {c |}{res}     30,387    .5284826    .4991963          0          1
{txt}distprevio~p {c |}{res}     30,387    2.016075    1.571696   7.86e-08         10
{txt}dissatisfied {c |}{res}     30,387    .2187119    .4133796          0          1
{txt}{space 2}closeparty {c |}{res}     30,387    .5183796    .4996703          0          1
{txt}p_government {c |}{res}     30,387    .5805772    .4934727          0          1
{txt}{hline 13}{c +}{hline 57}
{space 5}sd_rile {c |}{res}     30,387    17.14308    8.347989   3.830652   36.09492
{txt}lvotetotni~d {c |}{res}     30,387    18.88489    12.10005       3.69       50.6
{txt}lpss_mod3_~d {c |}{res}     30,387   -.0562247    1.014817  -3.671103   2.318012
{txt}{space 5}edudum1 {c |}{res}     30,387    .1975845    .3981835          0          1
{txt}{space 5}edudum2 {c |}{res}     30,387    .3716063    .4832419          0          1
{txt}{hline 13}{c +}{hline 57}
{space 5}edudum3 {c |}{res}     30,387    .4308092    .4951976          0          1
{txt}{space 5}incdum1 {c |}{res}     30,387    .3435022    .4748851          0          1
{txt}{space 5}incdum2 {c |}{res}     30,387    .4171192     .493091          0          1
{txt}{space 5}incdum3 {c |}{res}     30,387    .2393787    .4267113          0          1
{txt}{space 3}mainniche {c |}{res}     30,387     .071445     .257571          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}nichemain {c |}{res}     30,387    .0325468    .1774501          0          1
{txt}{space 1}mainradical {c |}{res}     30,387    .0441307    .2053889          0          1
{txt}{space 1}radicalmain {c |}{res}     30,387    .0191858      .13718          0          1
{txt}{space 3}maingreen {c |}{res}     30,387    .0188897    .1361376          0          1
{txt}{space 3}greenmain {c |}{res}     30,387    .0096423    .0977222          0          1
Click to Open File:  {browse "tablea4.rtf"}
{txt}
{com}. 
. restore
{txt}
{com}. 
. ************
. **Table A7**
. ************
. 
. melogit c_niche male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_niche==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-11457.326}  
Iteration 1:{space 3}log likelihood = {res:-10696.017}  
Iteration 2:{space 3}log likelihood = {res: -10684.16}  
Iteration 3:{space 3}log likelihood = {res:-10684.119}  
Iteration 4:{space 3}log likelihood = {res:-10684.119}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-10255.432}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-10255.432}  
Iteration 1:{space 3}log pseudolikelihood = {res:-10244.651}  
Iteration 2:{space 3}log pseudolikelihood = {res:-10196.053}  
Iteration 3:{space 3}log pseudolikelihood = {res:-10195.816}  
Iteration 4:{space 3}log pseudolikelihood = {res:-10195.816}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    44,930
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        53

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        81
{col 63}{txt}avg{col 67}={res}{col 69}     847.7
{col 63}{txt}max{col 67}={res}{col 69}     1,953

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   611.41
{txt}Log pseudolikelihood = {res}-10195.816{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:53} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.049516{col 39}{space 2} .0534948{col 50}{space 1}    0.95{col 59}{space 3}0.343{col 67}{space 4} .9497348{col 80}{space 3}  1.15978
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9839333{col 39}{space 2}  .001945{col 50}{space 1}   -8.19{col 59}{space 3}0.000{col 67}{space 4} .9801286{col 80}{space 3} .9877528
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.223718{col 39}{space 2} .0987414{col 50}{space 1}    2.50{col 59}{space 3}0.012{col 67}{space 4} 1.044716{col 80}{space 3}  1.43339
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.306492{col 39}{space 2} .1235448{col 50}{space 1}    2.83{col 59}{space 3}0.005{col 67}{space 4} 1.085463{col 80}{space 3} 1.572528
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8293817{col 39}{space 2} .0511007{col 50}{space 1}   -3.04{col 59}{space 3}0.002{col 67}{space 4} .7350372{col 80}{space 3} .9358357
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6288608{col 39}{space 2} .0501296{col 50}{space 1}   -5.82{col 59}{space 3}0.000{col 67}{space 4} .5378994{col 80}{space 3} .7352043
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9964313{col 39}{space 2} .0364133{col 50}{space 1}   -0.10{col 59}{space 3}0.922{col 67}{space 4} .9275585{col 80}{space 3} 1.070418
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3903069{col 39}{space 2} .0296531{col 50}{space 1}  -12.38{col 59}{space 3}0.000{col 67}{space 4}  .336308{col 80}{space 3} .4529761
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.236715{col 39}{space 2} .2188006{col 50}{space 1}    1.20{col 59}{space 3}0.230{col 67}{space 4} .8743283{col 80}{space 3} 1.749303
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.012583{col 39}{space 2} .0136777{col 50}{space 1}    0.93{col 59}{space 3}0.355{col 67}{space 4} .9861269{col 80}{space 3} 1.039749
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} 1.014922{col 39}{space 2} .0107473{col 50}{space 1}    1.40{col 59}{space 3}0.162{col 67}{space 4} .9940747{col 80}{space 3} 1.036207
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.227945{col 39}{space 2}  .134586{col 50}{space 1}    1.87{col 59}{space 3}0.061{col 67}{space 4} .9905694{col 80}{space 3} 1.522204
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .1119609{col 39}{space 2} .0400902{col 50}{space 1}   -6.11{col 59}{space 3}0.000{col 67}{space 4} .0554976{col 80}{space 3}   .22587
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .6424212{col 39}{space 2} .1558737{col 67}{space 4} .3992903{col 80}{space 3} 1.033596
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. tab country elec_year if e(sample)==1 & dataset=="EV"

                      {txt}{c |}                                                                 Election year
              Country {c |}      1976       1977       1979       1981       1982       1984       1985       1986       1988       1989       1991       1994       1998 {c |}     Total
{hline 22}{c +}{hline 143}{c +}{hline 10}
              Denmark {c |}{res}         0          0        487          0          0        457          0          0          0          0          0      1,079      1,148 {txt}{c |}{res}     3,171 
{txt}              Germany {c |}{res}         0          0          0          0          0          0          0          0          0          0          0        560          0 {txt}{c |}{res}       560 
{txt}          Netherlands {c |}{res}         0          0          0        871        471          0          0        837          0          0          0        384      1,062 {txt}{c |}{res}     3,625 
{txt}               Norway {c |}{res}         0        782          0        375          0          0      1,162          0          0      1,108          0          0          0 {txt}{c |}{res}     3,427 
{txt}               Sweden {c |}{res}     1,864          0      1,884          0      1,920          0      1,953          0      1,719          0      1,638      1,466      1,296 {txt}{c |}{res}    13,740 
{txt}{hline 22}{c +}{hline 143}{c +}{hline 10}
                Total {c |}{res}     1,864        782      2,371      1,246      2,391        457      3,115        837      1,719      1,108      1,638      3,489      3,506 {txt}{c |}{res}    24,523 
{txt}
{com}. tab country if e(sample)==1

                                {txt}Country {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                Austria {c |}{res}        349        0.78        0.78
{txt}                                 Canada {c |}{res}        149        0.33        1.11
{txt}                                Denmark {c |}{res}      4,866       10.83       11.94
{txt}                                Finland {c |}{res}      1,028        2.29       14.23
{txt}                                Germany {c |}{res}      4,236        9.43       23.65
{txt}                                Ireland {c |}{res}      1,656        3.69       27.34
{txt}                                  Italy {c |}{res}         81        0.18       27.52
{txt}                            Netherlands {c |}{res}      5,887       13.10       40.62
{txt}                            New Zealand {c |}{res}      1,587        3.53       44.16
{txt}                                 Norway {c |}{res}      5,216       11.61       55.76
{txt}                               Portugal {c |}{res}      1,274        2.84       58.60
{txt}                                  Spain {c |}{res}        422        0.94       59.54
{txt}                                 Sweden {c |}{res}     15,521       34.54       94.08
{txt}                            Switzerland {c |}{res}      1,560        3.47       97.56
{txt}                         United Kingdom {c |}{res}      1,098        2.44      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}     44,930      100.00
{txt}
{com}. gen mainniche=e(sample)
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}53
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_niche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3363.7216}  
Iteration 1:{space 3}log likelihood = {res:-3357.4813}  
Iteration 2:{space 3}log likelihood = {res:-3357.4789}  
Iteration 3:{space 3}log likelihood = {res:-3357.4789}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -3347.836}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -3347.836}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-3337.3891}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-3333.3198}  
Iteration 3:{space 3}log pseudolikelihood = {res:-3332.3345}  
Iteration 4:{space 3}log pseudolikelihood = {res:-3332.2528}  
Iteration 5:{space 3}log pseudolikelihood = {res:-3332.2525}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     6,473
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        45

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     143.8
{col 63}{txt}max{col 67}={res}{col 69}       421

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   250.49
{txt}Log pseudolikelihood = {res}-3332.2525{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:45} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8936896{col 39}{space 2} .0677906{col 50}{space 1}   -1.48{col 59}{space 3}0.138{col 67}{space 4} .7702276{col 80}{space 3} 1.036942
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9912993{col 39}{space 2} .0025581{col 50}{space 1}   -3.39{col 59}{space 3}0.001{col 67}{space 4} .9862982{col 80}{space 3} .9963257
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.107058{col 39}{space 2} .0994161{col 50}{space 1}    1.13{col 59}{space 3}0.257{col 67}{space 4} .9283904{col 80}{space 3} 1.320109
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8548944{col 39}{space 2} .0860244{col 50}{space 1}   -1.56{col 59}{space 3}0.119{col 67}{space 4} .7018747{col 80}{space 3} 1.041275
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.172215{col 39}{space 2} .1008316{col 50}{space 1}    1.85{col 59}{space 3}0.065{col 67}{space 4} .9903493{col 80}{space 3} 1.387477
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.684114{col 39}{space 2} .1781705{col 50}{space 1}    4.93{col 59}{space 3}0.000{col 67}{space 4} 1.368733{col 80}{space 3} 2.072164
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.163215{col 39}{space 2} .0446286{col 50}{space 1}    3.94{col 59}{space 3}0.000{col 67}{space 4} 1.078953{col 80}{space 3} 1.254059
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4493835{col 39}{space 2} .0356754{col 50}{space 1}  -10.08{col 59}{space 3}0.000{col 67}{space 4} .3846293{col 80}{space 3} .5250395
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7247266{col 39}{space 2} .1279526{col 50}{space 1}   -1.82{col 59}{space 3}0.068{col 67}{space 4}  .512734{col 80}{space 3} 1.024369
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9899089{col 39}{space 2} .0084789{col 50}{space 1}   -1.18{col 59}{space 3}0.236{col 67}{space 4} .9734294{col 80}{space 3} 1.006667
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9997522{col 39}{space 2} .0065188{col 50}{space 1}   -0.04{col 59}{space 3}0.970{col 67}{space 4} .9870568{col 80}{space 3} 1.012611
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .8950404{col 39}{space 2} .0396495{col 50}{space 1}   -2.50{col 59}{space 3}0.012{col 67}{space 4} .8206069{col 80}{space 3} .9762253
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .5467137{col 39}{space 2} .1684352{col 50}{space 1}   -1.96{col 59}{space 3}0.050{col 67}{space 4} .2988932{col 80}{space 3} 1.000009
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1302035{col 39}{space 2} .0646779{col 67}{space 4} .0491805{col 80}{space 3} .3447087
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen nichemain=e(sample)
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}45
{txt}
{com}. est store M2
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-8963.6309}  
Iteration 1:{space 3}log likelihood = {res:-7839.0582}  
Iteration 2:{space 3}log likelihood = {res:-7802.8702}  
Iteration 3:{space 3}log likelihood = {res: -7802.414}  
Iteration 4:{space 3}log likelihood = {res:-7802.4139}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-7253.8475}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-7253.8475}  
Iteration 1:{space 3}log pseudolikelihood = {res:-7220.6093}  
Iteration 2:{space 3}log pseudolikelihood = {res:-7207.9192}  
Iteration 3:{space 3}log pseudolikelihood = {res:-7206.9932}  
Iteration 4:{space 3}log pseudolikelihood = {res:-7206.9586}  
Iteration 5:{space 3}log pseudolikelihood = {res:-7206.9585}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    43,867
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        53

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        79
{col 63}{txt}avg{col 67}={res}{col 69}     827.7
{col 63}{txt}max{col 67}={res}{col 69}     1,953

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   623.08
{txt}Log pseudolikelihood = {res}-7206.9585{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:53} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.191478{col 40}{space 2} .0752868{col 51}{space 1}    2.77{col 60}{space 3}0.006{col 68}{space 4}  1.05269{col 81}{space 3} 1.348564
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9847529{col 40}{space 2} .0023899{col 51}{space 1}   -6.33{col 60}{space 3}0.000{col 68}{space 4} .9800799{col 81}{space 3} .9894482
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.276323{col 40}{space 2} .1321537{col 51}{space 1}    2.36{col 60}{space 3}0.018{col 68}{space 4} 1.041897{col 81}{space 3} 1.563493
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.077554{col 40}{space 2} .1284383{col 51}{space 1}    0.63{col 60}{space 3}0.531{col 68}{space 4} .8530621{col 81}{space 3} 1.361123
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7628927{col 40}{space 2} .0513773{col 51}{space 1}   -4.02{col 60}{space 3}0.000{col 68}{space 4} .6685578{col 81}{space 3} .8705385
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5335767{col 40}{space 2} .0401206{col 51}{space 1}   -8.35{col 60}{space 3}0.000{col 68}{space 4} .4604616{col 81}{space 3} .6183014
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.056178{col 40}{space 2} .0454902{col 51}{space 1}    1.27{col 60}{space 3}0.204{col 68}{space 4}  .970678{col 81}{space 3} 1.149208
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3850595{col 40}{space 2} .0377446{col 51}{space 1}   -9.74{col 60}{space 3}0.000{col 68}{space 4} .3177538{col 81}{space 3} .4666216
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.229013{col 40}{space 2} .2669538{col 51}{space 1}    0.95{col 60}{space 3}0.342{col 68}{space 4} .8029105{col 81}{space 3} 1.881248
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9664607{col 40}{space 2} .0236048{col 51}{space 1}   -1.40{col 60}{space 3}0.162{col 68}{space 4} .9212861{col 81}{space 3} 1.013851
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 1.069809{col 40}{space 2} .0303585{col 51}{space 1}    2.38{col 60}{space 3}0.017{col 68}{space 4} 1.011932{col 81}{space 3} 1.130997
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} 1.537843{col 40}{space 2} .3091386{col 51}{space 1}    2.14{col 60}{space 3}0.032{col 68}{space 4} 1.037057{col 81}{space 3} 2.280454
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0691261{col 40}{space 2} .0349102{col 51}{space 1}   -5.29{col 60}{space 3}0.000{col 68}{space 4} .0256901{col 81}{space 3} .1860022
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 1.761981{col 40}{space 2} .6478663{col 68}{space 4} .8570797{col 81}{space 3} 3.622273
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen mainradical=e(sample)
{txt}
{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}53
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2032.6317}  
Iteration 1:{space 3}log likelihood = {res:-2028.4504}  
Iteration 2:{space 3}log likelihood = {res:-2028.4482}  
Iteration 3:{space 3}log likelihood = {res:-2028.4482}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1995.3201}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1995.3201}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1990.0918}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1987.0147}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -1985.38}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1985.3722}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1985.3721}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     3,975
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        40

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}      99.4
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   314.99
{txt}Log pseudolikelihood = {res}-1985.3721{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:40} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2}  .933318{col 40}{space 2}  .094687{col 51}{space 1}   -0.68{col 60}{space 3}0.496{col 68}{space 4} .7650214{col 81}{space 3} 1.138638
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9896097{col 40}{space 2} .0031476{col 51}{space 1}   -3.28{col 60}{space 3}0.001{col 68}{space 4} .9834596{col 81}{space 3} .9957982
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.095257{col 40}{space 2} .1217622{col 51}{space 1}    0.82{col 60}{space 3}0.413{col 68}{space 4} .8808173{col 81}{space 3} 1.361902
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .6590194{col 40}{space 2} .0910435{col 51}{space 1}   -3.02{col 60}{space 3}0.003{col 68}{space 4} .5026952{col 81}{space 3} .8639561
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.182955{col 40}{space 2} .1570623{col 51}{space 1}    1.27{col 60}{space 3}0.206{col 68}{space 4} .9119121{col 81}{space 3} 1.534557
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.826804{col 40}{space 2} .2495212{col 51}{space 1}    4.41{col 60}{space 3}0.000{col 68}{space 4} 1.397743{col 81}{space 3} 2.387573
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.126227{col 40}{space 2} .0555504{col 51}{space 1}    2.41{col 60}{space 3}0.016{col 68}{space 4} 1.022447{col 81}{space 3}  1.24054
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .4418105{col 40}{space 2} .0414083{col 51}{space 1}   -8.72{col 60}{space 3}0.000{col 68}{space 4} .3676698{col 81}{space 3} .5309017
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.503522{col 40}{space 2} .5373488{col 51}{space 1}    1.14{col 60}{space 3}0.254{col 68}{space 4} .7462697{col 81}{space 3} 3.029169
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9743668{col 40}{space 2} .0145597{col 51}{space 1}   -1.74{col 60}{space 3}0.082{col 68}{space 4} .9462441{col 81}{space 3} 1.003325
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9850901{col 40}{space 2} .0139285{col 51}{space 1}   -1.06{col 60}{space 3}0.288{col 68}{space 4} .9581656{col 81}{space 3} 1.012771
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2}  .922963{col 40}{space 2} .0877383{col 51}{space 1}   -0.84{col 60}{space 3}0.399{col 68}{space 4} .7660687{col 81}{space 3}  1.11199
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}  .871341{col 40}{space 2} .3314519{col 51}{space 1}   -0.36{col 60}{space 3}0.717{col 68}{space 4} .4134255{col 81}{space 3}  1.83645
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .3187594{col 40}{space 2}  .096594{col 68}{space 4}  .176004{col 81}{space 3} .5773027
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen radicalmain=e(sample)
{txt}
{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}40
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5552.3167}  
Iteration 1:{space 3}log likelihood = {res:-3342.5681}  
Iteration 2:{space 3}log likelihood = {res:-3245.9224}  
Iteration 3:{space 3}log likelihood = {res:-3190.0907}  
Iteration 4:{space 3}log likelihood = {res:-3189.9503}  
Iteration 5:{space 3}log likelihood = {res:-3189.9503}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-3037.6329}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-3037.6329}  
Iteration 1:{space 3}log pseudolikelihood = {res:-3021.9278}  
Iteration 2:{space 3}log pseudolikelihood = {res:-3011.0766}  
Iteration 3:{space 3}log pseudolikelihood = {res:-3009.2481}  
Iteration 4:{space 3}log pseudolikelihood = {res:-3008.8362}  
Iteration 5:{space 3}log pseudolikelihood = {res:-3008.8276}  
Iteration 6:{space 3}log pseudolikelihood = {res:-3008.8275}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    42,555
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        53

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        71
{col 63}{txt}avg{col 67}={res}{col 69}     802.9
{col 63}{txt}max{col 67}={res}{col 69}     1,921

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   453.11
{txt}Log pseudolikelihood = {res}-3008.8275{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:53} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .7041553{col 39}{space 2} .0520829{col 50}{space 1}   -4.74{col 59}{space 3}0.000{col 67}{space 4} .6091289{col 80}{space 3} .8140061
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9812059{col 39}{space 2} .0032965{col 50}{space 1}   -5.65{col 59}{space 3}0.000{col 67}{space 4} .9747662{col 80}{space 3} .9876882
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.207909{col 39}{space 2}   .23475{col 50}{space 1}    0.97{col 59}{space 3}0.331{col 67}{space 4} .8252929{col 80}{space 3}  1.76791
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 2.263977{col 39}{space 2} .4477364{col 50}{space 1}    4.13{col 59}{space 3}0.000{col 67}{space 4} 1.536504{col 80}{space 3} 3.335879
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2}  1.05688{col 39}{space 2}  .116184{col 50}{space 1}    0.50{col 59}{space 3}0.615{col 67}{space 4} .8520244{col 80}{space 3} 1.310989
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9755398{col 39}{space 2} .1702059{col 50}{space 1}   -0.14{col 59}{space 3}0.887{col 67}{space 4} .6929993{col 80}{space 3} 1.373274
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9264273{col 39}{space 2} .0410056{col 50}{space 1}   -1.73{col 59}{space 3}0.084{col 67}{space 4} .8494452{col 80}{space 3} 1.010386
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4479269{col 39}{space 2} .0526458{col 50}{space 1}   -6.83{col 59}{space 3}0.000{col 67}{space 4} .3557654{col 80}{space 3}  .563963
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.442175{col 39}{space 2} .3201501{col 50}{space 1}    1.65{col 59}{space 3}0.099{col 67}{space 4} .9333808{col 80}{space 3} 2.228319
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9963449{col 39}{space 2} .0394693{col 50}{space 1}   -0.09{col 59}{space 3}0.926{col 67}{space 4} .9219134{col 80}{space 3} 1.076786
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.962948{col 39}{space 2} .1908433{col 50}{space 1}    6.94{col 59}{space 3}0.000{col 67}{space 4}  1.62238{col 80}{space 3} 2.375007
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2}  .979631{col 39}{space 2} .2595134{col 50}{space 1}   -0.08{col 59}{space 3}0.938{col 67}{space 4} .5828695{col 80}{space 3}  1.64647
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0007068{col 39}{space 2}  .000659{col 50}{space 1}   -7.78{col 59}{space 3}0.000{col 67}{space 4} .0001137{col 80}{space 3} .0043946
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2}  2.96697{col 39}{space 2} 1.002686{col 67}{space 4} 1.529855{col 80}{space 3} 5.754083
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. gen maingreen=e(sample)
{txt}
{com}. estadd scalar N_elections= e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}53
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -769.0618}  
Iteration 1:{space 3}log likelihood = {res:-768.44431}  
Iteration 2:{space 3}log likelihood = {res:-768.44419}  
Iteration 3:{space 3}log likelihood = {res:-768.44419}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-778.25921}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-778.25921}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-773.47058}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res: -768.8566}  
Iteration 3:{space 3}log pseudolikelihood = {res:-767.92826}  
Iteration 4:{space 3}log pseudolikelihood = {res:-767.72384}  
Iteration 5:{space 3}log pseudolikelihood = {res:-767.72135}  
Iteration 6:{space 3}log pseudolikelihood = {res:-767.72135}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,320
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        25

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      52.8
{col 63}{txt}max{col 67}={res}{col 69}       205

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   117.60
{txt}Log pseudolikelihood = {res}-767.72135{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:25} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8823413{col 39}{space 2} .0929708{col 50}{space 1}   -1.19{col 59}{space 3}0.235{col 67}{space 4} .7177065{col 80}{space 3} 1.084742
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9994674{col 39}{space 2} .0053195{col 50}{space 1}   -0.10{col 59}{space 3}0.920{col 67}{space 4} .9890957{col 80}{space 3} 1.009948
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .8438354{col 39}{space 2} .1875923{col 50}{space 1}   -0.76{col 59}{space 3}0.445{col 67}{space 4} .5457929{col 80}{space 3} 1.304631
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .6819553{col 39}{space 2} .1571354{col 50}{space 1}   -1.66{col 59}{space 3}0.097{col 67}{space 4} .4341332{col 80}{space 3} 1.071245
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8698972{col 39}{space 2} .1230976{col 50}{space 1}   -0.98{col 59}{space 3}0.325{col 67}{space 4} .6591981{col 80}{space 3} 1.147942
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.147341{col 39}{space 2} .2228265{col 50}{space 1}    0.71{col 59}{space 3}0.479{col 67}{space 4} .7841143{col 80}{space 3} 1.678825
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.314254{col 39}{space 2} .0821473{col 50}{space 1}    4.37{col 59}{space 3}0.000{col 67}{space 4}  1.16272{col 80}{space 3} 1.485537
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2}  .511914{col 39}{space 2}  .078281{col 50}{space 1}   -4.38{col 59}{space 3}0.000{col 67}{space 4} .3793435{col 80}{space 3} .6908143
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .8543865{col 39}{space 2} .1532576{col 50}{space 1}   -0.88{col 59}{space 3}0.380{col 67}{space 4} .6011297{col 80}{space 3} 1.214341
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9987322{col 39}{space 2} .0121617{col 50}{space 1}   -0.10{col 59}{space 3}0.917{col 67}{space 4} .9751778{col 80}{space 3} 1.022856
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9588358{col 39}{space 2} .0382138{col 50}{space 1}   -1.05{col 59}{space 3}0.292{col 67}{space 4} .8867886{col 80}{space 3} 1.036736
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.017658{col 39}{space 2} .0494841{col 50}{space 1}    0.36{col 59}{space 3}0.719{col 67}{space 4} .9251491{col 80}{space 3} 1.119417
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .7742263{col 39}{space 2} .3077018{col 50}{space 1}   -0.64{col 59}{space 3}0.520{col 67}{space 4} .3552824{col 80}{space 3} 1.687183
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .0462594{col 39}{space 2} .0679013{col 67}{space 4} .0026048{col 80}{space 3}  .821533
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen greenmain=e(sample)
{txt}
{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}25
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea7.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A7. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea7.rtf"'})

{com}. 
. ************
. **Table A8**
. ************
. 
. melogit c_comm_vs_mainstream_left male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_comm_vs_mainstream_left==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2044.2837}  
Iteration 1:{space 3}log likelihood = {res:-1820.2177}  
Iteration 2:{space 3}log likelihood = {res:-1816.6896}  
Iteration 3:{space 3}log likelihood = {res:-1816.6753}  
Iteration 4:{space 3}log likelihood = {res:-1816.6753}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1667.5394}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1667.5394}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1653.1005}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1649.9041}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1649.5671}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1649.5596}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1649.5596}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     8,825
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        38

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}     232.2
{col 63}{txt}max{col 67}={res}{col 69}       559

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   511.16
{txt}Log pseudolikelihood = {res}-1649.5596{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:38} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}c_comm_vs_mainstream_left{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .902895{col 39}{space 2}  .117905{col 50}{space 1}   -0.78{col 59}{space 3}0.434{col 67}{space 4} .6990088{col 80}{space 3}  1.16625
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9813314{col 39}{space 2} .0045532{col 50}{space 1}   -4.06{col 59}{space 3}0.000{col 67}{space 4} .9724477{col 80}{space 3} .9902962
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.902823{col 39}{space 2} .3496524{col 50}{space 1}    3.50{col 59}{space 3}0.000{col 67}{space 4} 1.327353{col 80}{space 3} 2.727788
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 2.473181{col 39}{space 2} .5548448{col 50}{space 1}    4.04{col 59}{space 3}0.000{col 67}{space 4} 1.593285{col 80}{space 3} 3.839002
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.206846{col 39}{space 2} .2920801{col 50}{space 1}    5.98{col 59}{space 3}0.000{col 67}{space 4} 1.702605{col 80}{space 3} 2.860422
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8257796{col 39}{space 2} .0837053{col 50}{space 1}   -1.89{col 59}{space 3}0.059{col 67}{space 4} .6769896{col 80}{space 3} 1.007271
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .7945408{col 39}{space 2}  .122477{col 50}{space 1}   -1.49{col 59}{space 3}0.136{col 67}{space 4}  .587361{col 80}{space 3} 1.074799
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.050391{col 39}{space 2} .0852279{col 50}{space 1}    0.61{col 59}{space 3}0.545{col 67}{space 4} .8959526{col 80}{space 3}  1.23145
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2}  .380324{col 39}{space 2} .0600389{col 50}{space 1}   -6.12{col 59}{space 3}0.000{col 67}{space 4} .2791136{col 80}{space 3} .5182348
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.123832{col 39}{space 2} .7427317{col 50}{space 1}    0.18{col 59}{space 3}0.860{col 67}{space 4} .3077151{col 80}{space 3} 4.104439
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9285368{col 39}{space 2} .0400859{col 50}{space 1}   -1.72{col 59}{space 3}0.086{col 67}{space 4} .8532019{col 80}{space 3} 1.010523
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9938806{col 39}{space 2} .0849937{col 50}{space 1}   -0.07{col 59}{space 3}0.943{col 67}{space 4} .8405082{col 80}{space 3}  1.17524
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 2.110627{col 39}{space 2}  .584442{col 50}{space 1}    2.70{col 59}{space 3}0.007{col 67}{space 4}  1.22662{col 80}{space 3} 3.631725
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .1424432{col 39}{space 2} .1248102{col 50}{space 1}   -2.22{col 59}{space 3}0.026{col 67}{space 4} .0255747{col 80}{space 3} .7933651
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 2.891316{col 39}{space 2} 1.391087{col 67}{space 4} 1.126058{col 80}{space 3} 7.423874
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}38
{txt}
{com}. melogit c_mainstream_left_vs_comm male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_comm_vs_mainstream_left==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-686.83384}  
Iteration 1:{space 3}log likelihood = {res:-680.46793}  
Iteration 2:{space 3}log likelihood = {res:-680.43325}  
Iteration 3:{space 3}log likelihood = {res:-680.43325}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-669.65078}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-669.65078}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-666.78766}  
Iteration 2:{space 3}log pseudolikelihood = {res:-665.41217}  
Iteration 3:{space 3}log pseudolikelihood = {res:-665.40553}  
Iteration 4:{space 3}log pseudolikelihood = {res:-665.40552}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,603
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      66.8
{col 63}{txt}max{col 67}={res}{col 69}       162

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   293.50
{txt}Log pseudolikelihood = {res}-665.40552{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:24} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}c_mainstream_left_vs_comm{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8477242{col 39}{space 2} .1577735{col 50}{space 1}   -0.89{col 59}{space 3}0.375{col 67}{space 4} .5886184{col 80}{space 3} 1.220887
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9964602{col 39}{space 2} .0070599{col 50}{space 1}   -0.50{col 59}{space 3}0.617{col 67}{space 4} .9827186{col 80}{space 3} 1.010394
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.189043{col 39}{space 2} .2828551{col 50}{space 1}    0.73{col 59}{space 3}0.467{col 67}{space 4} .7459498{col 80}{space 3} 1.895332
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .7616002{col 39}{space 2} .1728048{col 50}{space 1}   -1.20{col 59}{space 3}0.230{col 67}{space 4} .4881935{col 80}{space 3} 1.188125
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .6430528{col 39}{space 2} .0852015{col 50}{space 1}   -3.33{col 59}{space 3}0.001{col 67}{space 4} .4959825{col 80}{space 3} .8337329
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.185292{col 39}{space 2}  .222857{col 50}{space 1}    0.90{col 59}{space 3}0.366{col 67}{space 4} .8199427{col 80}{space 3} 1.713433
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.799659{col 39}{space 2} .3176798{col 50}{space 1}    3.33{col 59}{space 3}0.001{col 67}{space 4} 1.273311{col 80}{space 3} 2.543585
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9796244{col 39}{space 2} .0538155{col 50}{space 1}   -0.37{col 59}{space 3}0.708{col 67}{space 4} .8796279{col 80}{space 3} 1.090989
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3959771{col 39}{space 2}  .064888{col 50}{space 1}   -5.65{col 59}{space 3}0.000{col 67}{space 4} .2872005{col 80}{space 3} .5459525
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.828269{col 39}{space 2}  1.60258{col 50}{space 1}    0.69{col 59}{space 3}0.491{col 67}{space 4} .3280307{col 80}{space 3} 10.18981
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.020069{col 39}{space 2} .0202791{col 50}{space 1}    1.00{col 59}{space 3}0.318{col 67}{space 4}  .981087{col 80}{space 3}   1.0606
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2}  .962551{col 39}{space 2} .0476725{col 50}{space 1}   -0.77{col 59}{space 3}0.441{col 67}{space 4} .8735064{col 80}{space 3} 1.060673
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .7998787{col 39}{space 2} .1140444{col 50}{space 1}   -1.57{col 59}{space 3}0.117{col 67}{space 4} .6048703{col 80}{space 3} 1.057757
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .3033839{col 39}{space 2} .1810429{col 50}{space 1}   -2.00{col 59}{space 3}0.046{col 67}{space 4}  .094198{col 80}{space 3} .9771092
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .3808992{col 39}{space 2} .1392992{col 67}{space 4} .1860013{col 80}{space 3} .7800173
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}24
{txt}
{com}. melogit c_rr_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_rr_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3576.1009}  
Iteration 1:{space 3}log likelihood = {res:-2519.8226}  
Iteration 2:{space 3}log likelihood = {res:-2468.3495}  
Iteration 3:{space 3}log likelihood = {res:-2466.0523}  
Iteration 4:{space 3}log likelihood = {res:-2466.0449}  
Iteration 5:{space 3}log likelihood = {res:-2466.0449}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2137.0888}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2137.0888}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2058.8311}  
Iteration 2:{space 3}log pseudolikelihood = {res: -2039.246}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2033.1535}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2031.1519}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2030.1755}  
Iteration 6:{space 3}log pseudolikelihood = {res:-2029.9033}  
Iteration 7:{space 3}log pseudolikelihood = {res:-2029.9088}  
Iteration 8:{space 3}log pseudolikelihood = {res:-2029.9197}  
Iteration 9:{space 3}log pseudolikelihood = {res:-2029.9258}  
Iteration 10:{space 2}log pseudolikelihood = {res:-2029.9288}  
Iteration 11:{space 2}log pseudolikelihood = {res:-2029.9301}  
Iteration 12:{space 2}log pseudolikelihood = {res:-2029.9307}  
Iteration 13:{space 2}log pseudolikelihood = {res: -2029.931}  
Iteration 14:{space 2}log pseudolikelihood = {res:-2029.9311}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,279
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     622.5
{col 63}{txt}max{col 67}={res}{col 69}     1,198

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   461.27
{txt}Log pseudolikelihood = {res}-2029.9311{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}       c_rr_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 2.073332{col 39}{space 2}  .208953{col 50}{space 1}    7.24{col 59}{space 3}0.000{col 67}{space 4} 1.701703{col 80}{space 3}  2.52612
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9933992{col 39}{space 2} .0039983{col 50}{space 1}   -1.65{col 59}{space 3}0.100{col 67}{space 4} .9855935{col 80}{space 3} 1.001267
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.187177{col 39}{space 2} .1733018{col 50}{space 1}    1.18{col 59}{space 3}0.240{col 67}{space 4} .8917818{col 80}{space 3} 1.580419
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .7664955{col 39}{space 2}  .169406{col 50}{space 1}   -1.20{col 59}{space 3}0.229{col 67}{space 4} .4970298{col 80}{space 3} 1.182052
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2}  2.95831{col 39}{space 2} .4651051{col 50}{space 1}    6.90{col 59}{space 3}0.000{col 67}{space 4} 2.173791{col 80}{space 3}  4.02596
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .7068525{col 39}{space 2} .0870299{col 50}{space 1}   -2.82{col 59}{space 3}0.005{col 67}{space 4} .5552981{col 80}{space 3} .8997698
{txt}{space 13}High income  {c |}{col 27}{res}{space 2}  .566674{col 39}{space 2} .0814627{col 50}{space 1}   -3.95{col 59}{space 3}0.000{col 67}{space 4} .4275316{col 80}{space 3} .7511011
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.192976{col 39}{space 2} .0509452{col 50}{space 1}    4.13{col 59}{space 3}0.000{col 67}{space 4}  1.09719{col 80}{space 3} 1.297125
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .2970512{col 39}{space 2} .0505962{col 50}{space 1}   -7.13{col 59}{space 3}0.000{col 67}{space 4} .2127393{col 80}{space 3} .4147774
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.155768{col 39}{space 2} .2810663{col 50}{space 1}    0.60{col 59}{space 3}0.552{col 67}{space 4} .7175808{col 80}{space 3} 1.861533
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.059989{col 39}{space 2} .0673514{col 50}{space 1}    0.92{col 59}{space 3}0.359{col 67}{space 4} .9358721{col 80}{space 3} 1.200568
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.242089{col 39}{space 2}  .155834{col 50}{space 1}    1.73{col 59}{space 3}0.084{col 67}{space 4} .9713148{col 80}{space 3} 1.588348
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 9.459377{col 39}{space 2} 6.960955{col 50}{space 1}    3.05{col 59}{space 3}0.002{col 67}{space 4} 2.236049{col 80}{space 3} 40.01692
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0000929{col 39}{space 2} .0001274{col 50}{space 1}   -6.77{col 59}{space 3}0.000{col 67}{space 4} 6.32e-06{col 80}{space 3} .0013667
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2}  13.9238{col 39}{space 2} 4.788642{col 67}{space 4} 7.096016{col 80}{space 3} 27.32129
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. melogit c_mainstream_vs_rr male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_rr_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-455.68572}  
Iteration 1:{space 3}log likelihood = {res:-453.21024}  
Iteration 2:{space 3}log likelihood = {res: -453.1977}  
Iteration 3:{space 3}log likelihood = {res: -453.1977}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-450.59834}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-450.59834}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-448.10247}  
Iteration 2:{space 3}log pseudolikelihood = {res:-447.17921}  
Iteration 3:{space 3}log pseudolikelihood = {res:-446.77773}  
Iteration 4:{space 3}log pseudolikelihood = {res:-446.76559}  
Iteration 5:{space 3}log pseudolikelihood = {res:-446.76555}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       963
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        12

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         2
{col 63}{txt}avg{col 67}={res}{col 69}      80.3
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-446.76555{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 91:(Std. err. adjusted for {res:12} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}       c_mainstream_vs_rr{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8687925{col 39}{space 2} .1527137{col 50}{space 1}   -0.80{col 59}{space 3}0.424{col 67}{space 4} .6155941{col 80}{space 3} 1.226133
{txt}{space 22}age {c |}{col 27}{res}{space 2}  .986464{col 39}{space 2} .0060134{col 50}{space 1}   -2.24{col 59}{space 3}0.025{col 67}{space 4} .9747482{col 80}{space 3} .9983207
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.638511{col 39}{space 2} .4592014{col 50}{space 1}    1.76{col 59}{space 3}0.078{col 67}{space 4} .9460094{col 80}{space 3} 2.837939
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8846141{col 39}{space 2}  .324599{col 50}{space 1}   -0.33{col 59}{space 3}0.738{col 67}{space 4} .4309385{col 80}{space 3} 1.815902
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .6788725{col 39}{space 2} .1077904{col 50}{space 1}   -2.44{col 59}{space 3}0.015{col 67}{space 4} .4973197{col 80}{space 3} .9267035
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.319185{col 39}{space 2} .3997831{col 50}{space 1}    0.91{col 59}{space 3}0.361{col 67}{space 4} .7283603{col 80}{space 3} 2.389268
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 2.043912{col 39}{space 2} .5716022{col 50}{space 1}    2.56{col 59}{space 3}0.011{col 67}{space 4} 1.181447{col 80}{space 3} 3.535981
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9640853{col 39}{space 2} .0965339{col 50}{space 1}   -0.37{col 59}{space 3}0.715{col 67}{space 4} .7922908{col 80}{space 3}  1.17313
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4049145{col 39}{space 2} .0782274{col 50}{space 1}   -4.68{col 59}{space 3}0.000{col 67}{space 4} .2772781{col 80}{space 3} .5913043
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.592952{col 39}{space 2} 1.060709{col 50}{space 1}    0.70{col 59}{space 3}0.484{col 67}{space 4} .4319246{col 80}{space 3} 5.874859
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9491644{col 39}{space 2} .0264716{col 50}{space 1}   -1.87{col 59}{space 3}0.061{col 67}{space 4} .8986736{col 80}{space 3} 1.002492
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9639489{col 39}{space 2} .0646815{col 50}{space 1}   -0.55{col 59}{space 3}0.584{col 67}{space 4} .8451581{col 80}{space 3} 1.099436
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .9317678{col 39}{space 2} .2400601{col 50}{space 1}   -0.27{col 59}{space 3}0.784{col 67}{space 4} .5623473{col 80}{space 3}  1.54387
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 1.827049{col 39}{space 2} 1.043841{col 50}{space 1}    1.05{col 59}{space 3}0.291{col 67}{space 4} .5962611{col 80}{space 3} 5.598401
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .2567213{col 39}{space 2}  .151653{col 67}{space 4} .0806551{col 80}{space 3} .8171319
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}12
{txt}
{com}. 
. esttab M1 M2 M3 M4 using "tablea8.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Radical left" "Radical left-->Main" "Main-->Radical right" "Radical right-->Main") scalar(N_elections) title(Table A8. Individual families (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea8.rtf"'})

{com}. 
. ************
. **Table A9**
. ************
. 
. melogit c_green_vs_mainstream_left male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream_left==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1642.3957}  
Iteration 1:{space 3}log likelihood = {res:-1285.0029}  
Iteration 2:{space 3}log likelihood = {res:-1265.3475}  
Iteration 3:{space 3}log likelihood = {res:-1264.4917}  
Iteration 4:{space 3}log likelihood = {res:-1264.4893}  
Iteration 5:{space 3}log likelihood = {res:-1264.4893}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1244.5653}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1244.5653}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1241.0561}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1238.0022}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1237.7409}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1237.7352}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1237.7352}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     8,667
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        38

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         4
{col 63}{txt}avg{col 67}={res}{col 69}     228.1
{col 63}{txt}max{col 67}={res}{col 69}       589

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   292.90
{txt}Log pseudolikelihood = {res}-1237.7352{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:38} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}c_green_vs_mainstream_left{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .7148415{col 40}{space 2}  .055849{col 51}{space 1}   -4.30{col 60}{space 3}0.000{col 68}{space 4} .6133484{col 81}{space 3} .8331289
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9785962{col 40}{space 2} .0043822{col 51}{space 1}   -4.83{col 60}{space 3}0.000{col 68}{space 4} .9700448{col 81}{space 3} .9872229
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.451114{col 40}{space 2} .3859686{col 51}{space 1}    1.40{col 60}{space 3}0.162{col 68}{space 4} .8615854{col 81}{space 3} 2.444021
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 3.268677{col 40}{space 2} 1.357523{col 51}{space 1}    2.85{col 60}{space 3}0.004{col 68}{space 4} 1.448295{col 81}{space 3}  7.37712
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 1.175794{col 40}{space 2}  .177613{col 51}{space 1}    1.07{col 60}{space 3}0.284{col 68}{space 4} .8744815{col 81}{space 3} 1.580928
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.177576{col 40}{space 2} .1815466{col 51}{space 1}    1.06{col 60}{space 3}0.289{col 68}{space 4} .8704813{col 81}{space 3}  1.59301
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.311207{col 40}{space 2} .2272197{col 51}{space 1}    1.56{col 60}{space 3}0.118{col 68}{space 4} .9336115{col 81}{space 3}  1.84152
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} .9126923{col 40}{space 2} .0381011{col 51}{space 1}   -2.19{col 60}{space 3}0.029{col 68}{space 4} .8409888{col 81}{space 3} .9905093
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .5702233{col 40}{space 2} .0774006{col 51}{space 1}   -4.14{col 60}{space 3}0.000{col 68}{space 4} .4370239{col 81}{space 3} .7440204
{txt}{space 14}p_government {c |}{col 28}{res}{space 2}  .634883{col 40}{space 2} .3599343{col 51}{space 1}   -0.80{col 60}{space 3}0.423{col 68}{space 4} .2089882{col 81}{space 3} 1.928704
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9521014{col 40}{space 2} .0316689{col 51}{space 1}   -1.48{col 60}{space 3}0.140{col 68}{space 4} .8920114{col 81}{space 3} 1.016239
{txt}{space 4}lvotetotgreen_combined {c |}{col 28}{res}{space 2} 1.697548{col 40}{space 2} .1756671{col 51}{space 1}    5.11{col 60}{space 3}0.000{col 68}{space 4} 1.385917{col 81}{space 3}  2.07925
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} .7138134{col 40}{space 2} .1425669{col 51}{space 1}   -1.69{col 60}{space 3}0.091{col 68}{space 4} .4825895{col 81}{space 3} 1.055824
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0094501{col 40}{space 2} .0083474{col 51}{space 1}   -5.28{col 60}{space 3}0.000{col 68}{space 4} .0016732{col 81}{space 3}  .053372
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 1.218666{col 40}{space 2} .5731162{col 68}{space 4} .4848199{col 81}{space 3} 3.063297
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}38
{txt}
{com}. 
. melogit c_mainstream_left_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream_left==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-436.98283}  
Iteration 1:{space 3}log likelihood = {res:-436.28518}  
Iteration 2:{space 3}log likelihood = {res:-436.28469}  
Iteration 3:{space 3}log likelihood = {res:-436.28469}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-442.09578}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-442.09578}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res: -436.1012}  
Iteration 2:{space 3}log pseudolikelihood = {res:-435.82391}  
Iteration 3:{space 3}log pseudolikelihood = {res:-435.75312}  
Iteration 4:{space 3}log pseudolikelihood = {res: -435.7518}  
Iteration 5:{space 3}log pseudolikelihood = {res: -435.7518}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       926
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        21

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      44.1
{col 63}{txt}max{col 67}={res}{col 69}       169

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}    34.73
{txt}Log pseudolikelihood = {res}-435.7518{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0009
{txt}{ralign 92:(Std. err. adjusted for {res:21} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}c_mainstream_left_vs_green{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8628806{col 40}{space 2} .1567691{col 51}{space 1}   -0.81{col 60}{space 3}0.417{col 68}{space 4} .6043708{col 81}{space 3} 1.231964
{txt}{space 23}age {c |}{col 28}{res}{space 2} 1.001446{col 40}{space 2} .0085282{col 51}{space 1}    0.17{col 60}{space 3}0.865{col 68}{space 4} .9848701{col 81}{space 3} 1.018302
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} .8542653{col 40}{space 2} .2945487{col 51}{space 1}   -0.46{col 60}{space 3}0.648{col 68}{space 4} .4346108{col 81}{space 3} 1.679133
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7318822{col 40}{space 2} .2990701{col 51}{space 1}   -0.76{col 60}{space 3}0.445{col 68}{space 4}  .328559{col 81}{space 3} 1.630305
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 1.188826{col 40}{space 2} .2355529{col 51}{space 1}    0.87{col 60}{space 3}0.383{col 68}{space 4} .8062359{col 81}{space 3} 1.752971
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .9610907{col 40}{space 2} .2028263{col 51}{space 1}   -0.19{col 60}{space 3}0.851{col 68}{space 4} .6355195{col 81}{space 3} 1.453449
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.015285{col 40}{space 2} .2767001{col 51}{space 1}    0.06{col 60}{space 3}0.956{col 68}{space 4} .5951224{col 81}{space 3} 1.732087
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.095189{col 40}{space 2} .0814975{col 51}{space 1}    1.22{col 60}{space 3}0.222{col 68}{space 4} .9465593{col 81}{space 3} 1.267158
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .7870364{col 40}{space 2} .1487246{col 51}{space 1}   -1.27{col 60}{space 3}0.205{col 68}{space 4} .5434318{col 81}{space 3} 1.139842
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.907266{col 40}{space 2} .4994889{col 51}{space 1}    2.47{col 60}{space 3}0.014{col 68}{space 4} 1.141544{col 81}{space 3}  3.18662
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} 1.048572{col 40}{space 2} .0143029{col 51}{space 1}    3.48{col 60}{space 3}0.001{col 68}{space 4}  1.02091{col 81}{space 3} 1.076983
{txt}{space 4}lvotetotgreen_combined {c |}{col 28}{res}{space 2} .9104629{col 40}{space 2} .0480273{col 51}{space 1}   -1.78{col 60}{space 3}0.075{col 68}{space 4} .8210339{col 81}{space 3} 1.009633
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} 1.160898{col 40}{space 2} .0851226{col 51}{space 1}    2.03{col 60}{space 3}0.042{col 68}{space 4} 1.005495{col 81}{space 3} 1.340319
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .2013261{col 40}{space 2} .1337667{col 51}{space 1}   -2.41{col 60}{space 3}0.016{col 68}{space 4} .0547442{col 81}{space 3} .7403928
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .0579644{col 40}{space 2} .0663269{col 68}{space 4}  .006154{col 81}{space 3} .5459659
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}21
{txt}
{com}. 
. melogit c_green_vs_mainstream_right male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream_right==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1655.1498}  
Iteration 1:{space 3}log likelihood = {res:-938.48005}  
Iteration 2:{space 3}log likelihood = {res:-914.45815}  
Iteration 3:{space 3}log likelihood = {res:-881.05649}  
Iteration 4:{space 3}log likelihood = {res:-880.76043}  
Iteration 5:{space 3}log likelihood = {res:-880.76017}  
Iteration 6:{space 3}log likelihood = {res:-880.76017}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-860.23743}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-860.23743}  
Iteration 1:{space 3}log pseudolikelihood = {res:-856.44988}  
Iteration 2:{space 3}log pseudolikelihood = {res:-855.24363}  
Iteration 3:{space 3}log pseudolikelihood = {res:-855.21079}  
Iteration 4:{space 3}log pseudolikelihood = {res:-855.21076}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    13,338
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        19
{col 63}{txt}avg{col 67}={res}{col 69}     342.0
{col 63}{txt}max{col 67}={res}{col 69}       763

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   452.41
{txt}Log pseudolikelihood = {res}-855.21076{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 93:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}c_green_vs_mainstream_right{col 29}{c |} Odds ratio{col 41}   std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}male {c |}{col 29}{res}{space 2}  .608554{col 41}{space 2} .0581674{col 52}{space 1}   -5.20{col 61}{space 3}0.000{col 69}{space 4} .5045901{col 82}{space 3} .7339384
{txt}{space 24}age {c |}{col 29}{res}{space 2} .9780749{col 41}{space 2} .0056503{col 52}{space 1}   -3.84{col 61}{space 3}0.000{col 69}{space 4} .9670629{col 82}{space 3} .9892122
{txt}{space 27} {c |}
{space 20}highedu {c |}
{space 7}Secondary education  {c |}{col 29}{res}{space 2} 3.100322{col 41}{space 2} 1.367155{col 52}{space 1}    2.57{col 61}{space 3}0.010{col 69}{space 4} 1.306324{col 82}{space 3} 7.358049
{txt}{space 2}Post-secondary education  {c |}{col 29}{res}{space 2} 4.761448{col 41}{space 2} 1.912294{col 52}{space 1}    3.89{col 61}{space 3}0.000{col 69}{space 4} 2.167101{col 82}{space 3} 10.46162
{txt}{space 27} {c |}
{space 15}dissatisfied {c |}{col 29}{res}{space 2} .9616965{col 41}{space 2} .2250359{col 52}{space 1}   -0.17{col 61}{space 3}0.867{col 69}{space 4} .6079359{col 82}{space 3} 1.521312
{txt}{space 27} {c |}
{space 16}income_3cat {c |}
{space 13}Medium income  {c |}{col 29}{res}{space 2} .9726936{col 41}{space 2} .3176112{col 52}{space 1}   -0.08{col 61}{space 3}0.932{col 69}{space 4} .5129031{col 82}{space 3} 1.844662
{txt}{space 15}High income  {c |}{col 29}{res}{space 2} .7866955{col 41}{space 2} .3452927{col 52}{space 1}   -0.55{col 61}{space 3}0.585{col 69}{space 4} .3328137{col 82}{space 3} 1.859568
{txt}{space 27} {c |}
{space 7}distpreviouspartycmp {c |}{col 29}{res}{space 2} .9373877{col 41}{space 2} .0899342{col 52}{space 1}   -0.67{col 61}{space 3}0.500{col 69}{space 4} .7767009{col 82}{space 3} 1.131318
{txt}{space 17}closeparty {c |}{col 29}{res}{space 2} .2756689{col 41}{space 2} .0491526{col 52}{space 1}   -7.23{col 61}{space 3}0.000{col 69}{space 4}  .194364{col 82}{space 3} .3909846
{txt}{space 15}p_government {c |}{col 29}{res}{space 2} 1.643855{col 41}{space 2} .4932744{col 52}{space 1}    1.66{col 61}{space 3}0.098{col 69}{space 4} .9129393{col 82}{space 3} 2.959954
{txt}{space 20}sd_rile {c |}{col 29}{res}{space 2}  1.00751{col 41}{space 2} .0312384{col 52}{space 1}    0.24{col 61}{space 3}0.809{col 69}{space 4} .9481074{col 82}{space 3} 1.070635
{txt}{space 5}lvotetotgreen_combined {c |}{col 29}{res}{space 2} 1.496938{col 41}{space 2} .1250684{col 52}{space 1}    4.83{col 61}{space 3}0.000{col 69}{space 4} 1.270827{col 82}{space 3}  1.76328
{txt}{space 14}lpss_mod3_upd {c |}{col 29}{res}{space 2} 1.019449{col 41}{space 2} .2039366{col 52}{space 1}    0.10{col 61}{space 3}0.923{col 69}{space 4} .6887893{col 82}{space 3} 1.508846
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .0016721{col 41}{space 2} .0015055{col 52}{space 1}   -7.10{col 61}{space 3}0.000{col 69}{space 4} .0002863{col 82}{space 3} .0097647
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec               {col 29}{txt}{c |}
{space 18}var(_cons){c |}{col 29}{res}{space 2} 1.288881{col 41}{space 2} .5136574{col 69}{space 4} .5901785{col 82}{space 3} 2.814763
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_right_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream_right==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-312.27628}  
Iteration 1:{space 3}log likelihood = {res:-301.08465}  
Iteration 2:{space 3}log likelihood = {res:-300.99106}  
Iteration 3:{space 3}log likelihood = {res:-300.99105}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-300.08934}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-300.08934}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-298.71974}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.55412}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.43052}  
Iteration 4:{space 3}log pseudolikelihood = {res:-297.42826}  
Iteration 5:{space 3}log pseudolikelihood = {res:-297.42826}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       871
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        21

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         2
{col 63}{txt}avg{col 67}={res}{col 69}      41.5
{col 63}{txt}max{col 67}={res}{col 69}       145

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   150.17
{txt}Log pseudolikelihood = {res}-297.42826{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 93:(Std. err. adjusted for {res:21} clusters in {res:country_elec})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}c_mainstream_right_vs_green{col 29}{c |} Odds ratio{col 41}   std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}male {c |}{col 29}{res}{space 2}  .627321{col 41}{space 2} .1341474{col 52}{space 1}   -2.18{col 61}{space 3}0.029{col 69}{space 4} .4125413{col 82}{space 3} .9539205
{txt}{space 24}age {c |}{col 29}{res}{space 2} .9980234{col 41}{space 2} .0071991{col 52}{space 1}   -0.27{col 61}{space 3}0.784{col 69}{space 4} .9840128{col 82}{space 3} 1.012234
{txt}{space 27} {c |}
{space 20}highedu {c |}
{space 7}Secondary education  {c |}{col 29}{res}{space 2} .9157831{col 41}{space 2} .5080564{col 52}{space 1}   -0.16{col 61}{space 3}0.874{col 69}{space 4} .3087199{col 82}{space 3} 2.716568
{txt}{space 2}Post-secondary education  {c |}{col 29}{res}{space 2}  .911313{col 41}{space 2} .5006084{col 52}{space 1}   -0.17{col 61}{space 3}0.866{col 69}{space 4} .3105131{col 82}{space 3} 2.674577
{txt}{space 27} {c |}
{space 15}dissatisfied {c |}{col 29}{res}{space 2} 1.294879{col 41}{space 2} .3399814{col 52}{space 1}    0.98{col 61}{space 3}0.325{col 69}{space 4} .7739968{col 82}{space 3} 2.166304
{txt}{space 27} {c |}
{space 16}income_3cat {c |}
{space 13}Medium income  {c |}{col 29}{res}{space 2} .9210197{col 41}{space 2} .2276748{col 52}{space 1}   -0.33{col 61}{space 3}0.739{col 69}{space 4} .5673526{col 82}{space 3}  1.49515
{txt}{space 15}High income  {c |}{col 29}{res}{space 2} .9861387{col 41}{space 2} .2343267{col 52}{space 1}   -0.06{col 61}{space 3}0.953{col 69}{space 4} .6189779{col 82}{space 3} 1.571089
{txt}{space 27} {c |}
{space 7}distpreviouspartycmp {c |}{col 29}{res}{space 2} 1.541801{col 41}{space 2} .1626177{col 52}{space 1}    4.10{col 61}{space 3}0.000{col 69}{space 4} 1.253862{col 82}{space 3} 1.895862
{txt}{space 17}closeparty {c |}{col 29}{res}{space 2} .2686447{col 41}{space 2} .0644407{col 52}{space 1}   -5.48{col 61}{space 3}0.000{col 69}{space 4} .1678795{col 82}{space 3} .4298917
{txt}{space 15}p_government {c |}{col 29}{res}{space 2} .3964078{col 41}{space 2} .2156448{col 52}{space 1}   -1.70{col 61}{space 3}0.089{col 69}{space 4} .1364868{col 82}{space 3} 1.151314
{txt}{space 20}sd_rile {c |}{col 29}{res}{space 2} .9369243{col 41}{space 2} .0261534{col 52}{space 1}   -2.33{col 61}{space 3}0.020{col 69}{space 4} .8870416{col 82}{space 3}  .989612
{txt}{space 5}lvotetotgreen_combined {c |}{col 29}{res}{space 2} .9452501{col 41}{space 2} .0765749{col 52}{space 1}   -0.70{col 61}{space 3}0.487{col 69}{space 4} .8064747{col 82}{space 3} 1.107905
{txt}{space 14}lpss_mod3_upd {c |}{col 29}{res}{space 2} .7820721{col 41}{space 2} .0954428{col 52}{space 1}   -2.01{col 61}{space 3}0.044{col 69}{space 4} .6156976{col 82}{space 3} .9934044
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .8521391{col 41}{space 2} .7648306{col 52}{space 1}   -0.18{col 61}{space 3}0.859{col 69}{space 4} .1467308{col 82}{space 3} 4.948797
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec               {col 29}{txt}{c |}
{space 18}var(_cons){c |}{col 29}{res}{space 2} .3054446{col 41}{space 2} .2044171{col 69}{space 4} .0822755{col 82}{space 3} 1.133951
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}21
{txt}
{com}. 
. esttab M1 M2 M3 M4 using "tablea9.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main left-->Green)" "Green-->Main left" "Main right-->Green" "Green-->Main right") scalar(N_elections) title(Table A9. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea9.rtf"'})

{com}. 
. *************
. **Table A10**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvol_switch lpss_mod3_upd if p_niche==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -5762.306}  
Iteration 1:{space 3}log likelihood = {res:-5503.9592}  
Iteration 2:{space 3}log likelihood = {res:-5502.9482}  
Iteration 3:{space 3}log likelihood = {res:-5502.9474}  
Iteration 4:{space 3}log likelihood = {res:-5502.9474}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -5374.971}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -5374.971}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-5371.4428}  
Iteration 2:{space 3}log pseudolikelihood = {res:-5362.9449}  
Iteration 3:{space 3}log pseudolikelihood = {res: -5340.389}  
Iteration 4:{space 3}log pseudolikelihood = {res: -5340.258}  
Iteration 5:{space 3}log pseudolikelihood = {res:-5340.2574}  
Iteration 6:{space 3}log pseudolikelihood = {res:-5340.2574}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    19,597
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       189
{col 63}{txt}avg{col 67}={res}{col 69}     725.8
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   403.72
{txt}Log pseudolikelihood = {res}-5340.2574{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  1.10301{col 39}{space 2} .0739734{col 50}{space 1}    1.46{col 59}{space 3}0.144{col 67}{space 4} .9671494{col 80}{space 3} 1.257955
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9834442{col 39}{space 2} .0027753{col 50}{space 1}   -5.92{col 59}{space 3}0.000{col 67}{space 4} .9780197{col 80}{space 3} .9888987
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.278772{col 39}{space 2} .1417317{col 50}{space 1}    2.22{col 59}{space 3}0.027{col 67}{space 4} 1.029084{col 80}{space 3} 1.589042
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.418626{col 39}{space 2} .2146234{col 50}{space 1}    2.31{col 59}{space 3}0.021{col 67}{space 4} 1.054605{col 80}{space 3} 1.908297
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .7656933{col 39}{space 2} .0608196{col 50}{space 1}   -3.36{col 59}{space 3}0.001{col 67}{space 4} .6553046{col 80}{space 3} .8946774
{txt}{space 13}High income  {c |}{col 27}{res}{space 2}  .581045{col 39}{space 2} .0629873{col 50}{space 1}   -5.01{col 59}{space 3}0.000{col 67}{space 4} .4698255{col 80}{space 3}  .718593
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.319803{col 39}{space 2} .1820273{col 50}{space 1}   10.72{col 59}{space 3}0.000{col 67}{space 4} 1.989116{col 80}{space 3} 2.705466
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9872256{col 39}{space 2} .0416571{col 50}{space 1}   -0.30{col 59}{space 3}0.761{col 67}{space 4} .9088642{col 80}{space 3} 1.072343
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3869321{col 39}{space 2} .0475761{col 50}{space 1}   -7.72{col 59}{space 3}0.000{col 67}{space 4} .3040699{col 80}{space 3} .4923752
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.140703{col 39}{space 2} .2589333{col 50}{space 1}    0.58{col 59}{space 3}0.562{col 67}{space 4} .7310626{col 80}{space 3}  1.77988
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2}   1.0172{col 39}{space 2} .0205384{col 50}{space 1}    0.84{col 59}{space 3}0.398{col 67}{space 4}  .977732{col 80}{space 3} 1.058262
{txt}{space 14}lvol_switch {c |}{col 27}{res}{space 2} 5.051771{col 39}{space 2}  9.14527{col 50}{space 1}    0.89{col 59}{space 3}0.371{col 67}{space 4} .1453802{col 80}{space 3} 175.5424
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.213645{col 39}{space 2} .1511465{col 50}{space 1}    1.55{col 59}{space 3}0.120{col 67}{space 4} .9507878{col 80}{space 3} 1.549172
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0967891{col 39}{space 2} .0691943{col 50}{space 1}   -3.27{col 59}{space 3}0.001{col 67}{space 4} .0238399{col 80}{space 3} .3929605
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4541628{col 39}{space 2} .1577832{col 67}{space 4} .2298745{col 80}{space 3} .8972888
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}27
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvol_switch lpss_mod3_upd if p_niche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1644.9463}  
Iteration 1:{space 3}log likelihood = {res:-1640.4248}  
Iteration 2:{space 3}log likelihood = {res:-1640.4197}  
Iteration 3:{space 3}log likelihood = {res:-1640.4197}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1637.7139}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1637.7139}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1632.9773}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1632.6662}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1631.6645}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1631.5848}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1631.5843}  
Iteration 6:{space 3}log pseudolikelihood = {res:-1631.5843}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     3,322
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        21

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        14
{col 63}{txt}avg{col 67}={res}{col 69}     158.2
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   346.29
{txt}Log pseudolikelihood = {res}-1631.5843{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:21} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .823177{col 39}{space 2} .1126354{col 50}{space 1}   -1.42{col 59}{space 3}0.155{col 67}{space 4} .6295398{col 80}{space 3} 1.076374
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9935004{col 39}{space 2} .0035971{col 50}{space 1}   -1.80{col 59}{space 3}0.072{col 67}{space 4} .9864752{col 80}{space 3} 1.000576
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.355244{col 39}{space 2} .2037771{col 50}{space 1}    2.02{col 59}{space 3}0.043{col 67}{space 4} 1.009321{col 80}{space 3} 1.819725
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.031585{col 39}{space 2} .1737016{col 50}{space 1}    0.18{col 59}{space 3}0.853{col 67}{space 4} .7416127{col 80}{space 3} 1.434938
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.169104{col 39}{space 2} .1393979{col 50}{space 1}    1.31{col 59}{space 3}0.190{col 67}{space 4} .9254656{col 80}{space 3} 1.476883
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.730354{col 39}{space 2} .2184508{col 50}{space 1}    4.34{col 59}{space 3}0.000{col 67}{space 4} 1.351058{col 80}{space 3} 2.216134
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .7116928{col 39}{space 2} .0721306{col 50}{space 1}   -3.36{col 59}{space 3}0.001{col 67}{space 4} .5834756{col 80}{space 3} .8680855
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.101596{col 39}{space 2} .0572458{col 50}{space 1}    1.86{col 59}{space 3}0.063{col 67}{space 4}  .994921{col 80}{space 3} 1.219709
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4112035{col 39}{space 2} .0505281{col 50}{space 1}   -7.23{col 59}{space 3}0.000{col 67}{space 4} .3231932{col 80}{space 3} .5231802
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .6706472{col 39}{space 2} .1879366{col 50}{space 1}   -1.43{col 59}{space 3}0.154{col 67}{space 4} .3872224{col 80}{space 3} 1.161523
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9886575{col 39}{space 2} .0304062{col 50}{space 1}   -0.37{col 59}{space 3}0.711{col 67}{space 4} .9308231{col 80}{space 3} 1.050085
{txt}{space 14}lvol_switch {c |}{col 27}{res}{space 2} .2365987{col 39}{space 2} .4483655{col 50}{space 1}   -0.76{col 59}{space 3}0.447{col 67}{space 4} .0057669{col 80}{space 3} 9.706861
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .8828188{col 39}{space 2} .1019587{col 50}{space 1}   -1.08{col 59}{space 3}0.281{col 67}{space 4} .7039866{col 80}{space 3} 1.107079
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .8495368{col 39}{space 2} .9980882{col 50}{space 1}   -0.14{col 59}{space 3}0.890{col 67}{space 4} .0849451{col 80}{space 3}  8.49623
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1950392{col 39}{space 2} .2426192{col 67}{space 4} .0170321{col 80}{space 3} 2.233443
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}21
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvol_switch lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -4368.554}  
Iteration 1:{space 3}log likelihood = {res:-3910.1966}  
Iteration 2:{space 3}log likelihood = {res:-3901.1472}  
Iteration 3:{space 3}log likelihood = {res:-3901.0918}  
Iteration 4:{space 3}log likelihood = {res:-3901.0918}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-3667.1443}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-3667.1443}  
Iteration 1:{space 3}log pseudolikelihood = {res:-3628.9158}  
Iteration 2:{space 3}log pseudolikelihood = {res:-3625.8298}  
Iteration 3:{space 3}log pseudolikelihood = {res:-3625.3916}  
Iteration 4:{space 3}log pseudolikelihood = {res:-3625.3677}  
Iteration 5:{space 3}log pseudolikelihood = {res:-3625.3676}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    18,975
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       181
{col 63}{txt}avg{col 67}={res}{col 69}     702.8
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   729.52
{txt}Log pseudolikelihood = {res}-3625.3676{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}c_radicalrl_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.337471{col 39}{space 2} .1064686{col 50}{space 1}    3.65{col 59}{space 3}0.000{col 67}{space 4} 1.144261{col 80}{space 3} 1.563306
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9857665{col 39}{space 2} .0030654{col 50}{space 1}   -4.61{col 59}{space 3}0.000{col 67}{space 4} .9797767{col 80}{space 3} .9917928
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.386771{col 39}{space 2} .2174984{col 50}{space 1}    2.08{col 59}{space 3}0.037{col 67}{space 4} 1.019774{col 80}{space 3} 1.885843
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.181691{col 39}{space 2} .2155924{col 50}{space 1}    0.92{col 59}{space 3}0.360{col 67}{space 4}  .826432{col 80}{space 3} 1.689664
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2}  2.70622{col 39}{space 2} .2824271{col 50}{space 1}    9.54{col 59}{space 3}0.000{col 67}{space 4} 2.205616{col 80}{space 3} 3.320446
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .7174716{col 39}{space 2} .0692055{col 50}{space 1}   -3.44{col 59}{space 3}0.001{col 67}{space 4} .5938818{col 80}{space 3} .8667812
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .5284012{col 39}{space 2} .0597163{col 50}{space 1}   -5.64{col 59}{space 3}0.000{col 67}{space 4} .4234155{col 80}{space 3}  .659418
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.045026{col 39}{space 2} .0511996{col 50}{space 1}    0.90{col 59}{space 3}0.369{col 67}{space 4} .9493444{col 80}{space 3} 1.150352
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3483024{col 39}{space 2} .0541322{col 50}{space 1}   -6.79{col 59}{space 3}0.000{col 67}{space 4} .2568415{col 80}{space 3} .4723325
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.124026{col 39}{space 2}  .293524{col 50}{space 1}    0.45{col 59}{space 3}0.654{col 67}{space 4} .6737469{col 80}{space 3} 1.875236
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9827654{col 39}{space 2} .0441288{col 50}{space 1}   -0.39{col 59}{space 3}0.699{col 67}{space 4} .8999713{col 80}{space 3} 1.073176
{txt}{space 14}lvol_switch {c |}{col 27}{res}{space 2} 177.8132{col 39}{space 2} 678.1501{col 50}{space 1}    1.36{col 59}{space 3}0.174{col 67}{space 4} .1008376{col 80}{space 3} 313549.1
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 3.019402{col 39}{space 2} 1.170579{col 50}{space 1}    2.85{col 59}{space 3}0.004{col 67}{space 4} 1.412285{col 80}{space 3} 6.455346
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0166154{col 39}{space 2} .0260286{col 50}{space 1}   -2.62{col 59}{space 3}0.009{col 67}{space 4}  .000771{col 80}{space 3} .3580497
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 2.711525{col 39}{space 2} 1.544771{col 67}{space 4} .8877272{col 80}{space 3} 8.282239
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}27
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvol_switch lpss_mod3_upd if p_radicalrl_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1043.5645}  
Iteration 1:{space 3}log likelihood = {res:-1040.1408}  
Iteration 2:{space 3}log likelihood = {res:-1040.1341}  
Iteration 3:{space 3}log likelihood = {res:-1040.1341}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1027.3876}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1027.3876}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1026.5265}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1023.884}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1023.6262}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1023.6216}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1023.6216}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,122
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        19

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     111.7
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   371.69
{txt}Log pseudolikelihood = {res}-1023.6216{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:19} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}c_mainstream_vs_radicalrl{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8556014{col 39}{space 2} .1479698{col 50}{space 1}   -0.90{col 59}{space 3}0.367{col 67}{space 4} .6096248{col 80}{space 3} 1.200827
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9913689{col 39}{space 2} .0045815{col 50}{space 1}   -1.88{col 59}{space 3}0.061{col 67}{space 4} .9824299{col 80}{space 3} 1.000389
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.102517{col 39}{space 2} .2076066{col 50}{space 1}    0.52{col 59}{space 3}0.604{col 67}{space 4}  .762258{col 80}{space 3} 1.594662
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .7778699{col 39}{space 2} .1797746{col 50}{space 1}   -1.09{col 59}{space 3}0.277{col 67}{space 4} .4945209{col 80}{space 3} 1.223571
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .6424974{col 39}{space 2} .0588862{col 50}{space 1}   -4.83{col 59}{space 3}0.000{col 67}{space 4} .5368549{col 80}{space 3} .7689282
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.195268{col 39}{space 2} .2268993{col 50}{space 1}    0.94{col 59}{space 3}0.347{col 67}{space 4} .8239114{col 80}{space 3} 1.734004
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.890799{col 39}{space 2} .3451338{col 50}{space 1}    3.49{col 59}{space 3}0.000{col 67}{space 4} 1.322126{col 80}{space 3} 2.704071
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.037122{col 39}{space 2} .0647708{col 50}{space 1}    0.58{col 59}{space 3}0.559{col 67}{space 4} .9176352{col 80}{space 3} 1.172167
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4168776{col 39}{space 2} .0554583{col 50}{space 1}   -6.58{col 59}{space 3}0.000{col 67}{space 4} .3211968{col 80}{space 3} .5410607
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .9386824{col 39}{space 2}  .462377{col 50}{space 1}   -0.13{col 59}{space 3}0.898{col 67}{space 4} .3574647{col 80}{space 3} 2.464928
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9747429{col 39}{space 2} .0314796{col 50}{space 1}   -0.79{col 59}{space 3}0.428{col 67}{space 4} .9149562{col 80}{space 3} 1.038436
{txt}{space 14}lvol_switch {c |}{col 27}{res}{space 2} .0723527{col 39}{space 2} .1448876{col 50}{space 1}   -1.31{col 59}{space 3}0.190{col 67}{space 4} .0014286{col 80}{space 3} 3.664386
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .8592814{col 39}{space 2} .2465505{col 50}{space 1}   -0.53{col 59}{space 3}0.597{col 67}{space 4} .4896697{col 80}{space 3} 1.507883
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 1.973426{col 39}{space 2} 2.299883{col 50}{space 1}    0.58{col 59}{space 3}0.560{col 67}{space 4} .2010056{col 80}{space 3} 19.37463
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4149107{col 39}{space 2} .2482072{col 67}{space 4} .1284548{col 80}{space 3} 1.340167
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}19
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvol_switch lpss_mod3_upd if p_green_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2634.5057}  
Iteration 1:{space 3}log likelihood = {res:-1821.0146}  
Iteration 2:{space 3}log likelihood = {res:-1778.3909}  
Iteration 3:{space 3}log likelihood = {res:-1771.7465}  
Iteration 4:{space 3}log likelihood = {res:-1771.5429}  
Iteration 5:{space 3}log likelihood = {res:-1771.5424}  
Iteration 6:{space 3}log likelihood = {res:-1771.5424}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1638.7065}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1638.7065}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1616.1569}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1609.2017}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1607.2145}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1606.8792}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1606.8627}  
Iteration 6:{space 3}log pseudolikelihood = {res:-1606.8628}  
Iteration 7:{space 3}log pseudolikelihood = {res: -1606.863}  
Iteration 8:{space 3}log pseudolikelihood = {res: -1606.863}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    18,257
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       184
{col 63}{txt}avg{col 67}={res}{col 69}     676.2
{col 63}{txt}max{col 67}={res}{col 69}     1,115

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   453.33
{txt}Log pseudolikelihood = {res}-1606.863{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .6735327{col 39}{space 2} .0576947{col 50}{space 1}   -4.61{col 59}{space 3}0.000{col 67}{space 4} .5694359{col 80}{space 3}  .796659
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9807114{col 39}{space 2} .0047139{col 50}{space 1}   -4.05{col 59}{space 3}0.000{col 67}{space 4} .9715157{col 80}{space 3}  .989994
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 3.189483{col 39}{space 2} 1.307615{col 50}{space 1}    2.83{col 59}{space 3}0.005{col 67}{space 4} 1.428063{col 80}{space 3}   7.1235
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 7.148804{col 39}{space 2} 2.847765{col 50}{space 1}    4.94{col 59}{space 3}0.000{col 67}{space 4} 3.274557{col 80}{space 3} 15.60681
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2}  1.30028{col 39}{space 2} .1712313{col 50}{space 1}    1.99{col 59}{space 3}0.046{col 67}{space 4} 1.004486{col 80}{space 3} 1.683178
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9412423{col 39}{space 2}   .15267{col 50}{space 1}   -0.37{col 59}{space 3}0.709{col 67}{space 4} .6849141{col 80}{space 3} 1.293501
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .7653479{col 39}{space 2} .2140132{col 50}{space 1}   -0.96{col 59}{space 3}0.339{col 67}{space 4} .4424239{col 80}{space 3} 1.323973
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9525103{col 39}{space 2} .0467421{col 50}{space 1}   -0.99{col 59}{space 3}0.321{col 67}{space 4} .8651652{col 80}{space 3} 1.048674
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5162347{col 39}{space 2} .0519327{col 50}{space 1}   -6.57{col 59}{space 3}0.000{col 67}{space 4} .4238549{col 80}{space 3} .6287489
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.332805{col 39}{space 2} .4601071{col 50}{space 1}    0.83{col 59}{space 3}0.405{col 67}{space 4} .6775122{col 80}{space 3} 2.621899
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9883084{col 39}{space 2} .0590791{col 50}{space 1}   -0.20{col 59}{space 3}0.844{col 67}{space 4} .8790415{col 80}{space 3} 1.111157
{txt}{space 14}lvol_switch {c |}{col 27}{res}{space 2} 83.22925{col 39}{space 2} 320.3543{col 50}{space 1}    1.15{col 59}{space 3}0.251{col 67}{space 4} .0440505{col 80}{space 3} 157253.9
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .5267883{col 39}{space 2}  .198006{col 50}{space 1}   -1.71{col 59}{space 3}0.088{col 67}{space 4}  .252169{col 80}{space 3} 1.100476
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0016029{col 39}{space 2} .0030199{col 50}{space 1}   -3.42{col 59}{space 3}0.001{col 67}{space 4} .0000399{col 80}{space 3} .0643559
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 7.264811{col 39}{space 2}  3.18006{col 67}{space 4} 3.080519{col 80}{space 3} 17.13266
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}27
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvol_switch lpss_mod3_upd if p_green_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-363.12609}  
Iteration 1:{space 3}log likelihood = {res:-362.82281}  
Iteration 2:{space 3}log likelihood = {res:-362.82277}  
Iteration 3:{space 3}log likelihood = {res:-362.82277}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-369.08617}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-369.08617}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-363.27234}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res: -362.8935}  
Iteration 3:{space 3}log pseudolikelihood = {res:-362.86688}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-362.84719}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-362.83269}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res: -362.8326}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-362.83256}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-362.83255}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res:-362.83254}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-362.83254}  (backed up)
Iteration 18:{space 2}log pseudolikelihood = {res:-362.83254}  (backed up)
Iteration 19:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-362.83254}  (backed up)
Iteration 21:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 23:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 25:{space 2}log pseudolikelihood = {res:-362.83254}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 27:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 28:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 29:{space 2}log pseudolikelihood = {res:-362.83254}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 32:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 33:{space 2}log pseudolikelihood = {res:-362.83254}  (backed up)
Iteration 34:{space 2}log pseudolikelihood = {res:-362.83254}  (not concave)
Iteration 35:{space 2}log pseudolikelihood = {res:-362.83254}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-362.83253}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res:-362.83252}  (not concave)
Iteration 38:{space 2}log pseudolikelihood = {res:-362.83251}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res: -362.8325}  (not concave)
Iteration 40:{space 2}log pseudolikelihood = {res: -362.8325}  
Iteration 41:{space 2}log pseudolikelihood = {res: -362.8322}  (backed up)
Iteration 42:{space 2}log pseudolikelihood = {res:-362.83106}  
Iteration 43:{space 2}log pseudolikelihood = {res:-362.83005}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res:-362.82995}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res:-362.82964}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-362.82952}  (not concave)
Iteration 47:{space 2}log pseudolikelihood = {res:-362.82932}  
Iteration 48:{space 2}log pseudolikelihood = {res:-362.82277}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-362.82277}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       648
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        14

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         6
{col 63}{txt}avg{col 67}={res}{col 69}      46.3
{col 63}{txt}max{col 67}={res}{col 69}       111

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}248893.32
{txt}Log pseudolikelihood = {res}-362.82277{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:14} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8107836{col 39}{space 2} .1418078{col 50}{space 1}   -1.20{col 59}{space 3}0.230{col 67}{space 4}  .575477{col 80}{space 3} 1.142305
{txt}{space 22}age {c |}{col 27}{res}{space 2} 1.003877{col 39}{space 2} .0077396{col 50}{space 1}    0.50{col 59}{space 3}0.616{col 67}{space 4} .9888221{col 80}{space 3} 1.019162
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.110431{col 39}{space 2}  .989269{col 50}{space 1}    0.12{col 59}{space 3}0.906{col 67}{space 4} .1937167{col 80}{space 3} 6.365262
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8102072{col 39}{space 2} .7504421{col 50}{space 1}   -0.23{col 59}{space 3}0.820{col 67}{space 4} .1318814{col 80}{space 3} 4.977469
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.101109{col 39}{space 2}  .251658{col 50}{space 1}    0.42{col 59}{space 3}0.673{col 67}{space 4} .7035399{col 80}{space 3} 1.723345
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9564646{col 39}{space 2} .1964182{col 50}{space 1}   -0.22{col 59}{space 3}0.828{col 67}{space 4} .6395396{col 80}{space 3} 1.430443
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.047765{col 39}{space 2} .2794364{col 50}{space 1}    0.17{col 59}{space 3}0.861{col 67}{space 4} .6212276{col 80}{space 3} 1.767166
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.329662{col 39}{space 2} .1185433{col 50}{space 1}    3.20{col 59}{space 3}0.001{col 67}{space 4} 1.116488{col 80}{space 3} 1.583537
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4347439{col 39}{space 2} .1081482{col 50}{space 1}   -3.35{col 59}{space 3}0.001{col 67}{space 4} .2669845{col 80}{space 3} .7079148
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.613879{col 39}{space 2} .6916006{col 50}{space 1}    1.12{col 59}{space 3}0.264{col 67}{space 4} .6967912{col 80}{space 3} 3.737998
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9882345{col 39}{space 2} .0219152{col 50}{space 1}   -0.53{col 59}{space 3}0.594{col 67}{space 4} .9462015{col 80}{space 3} 1.032135
{txt}{space 14}lvol_switch {c |}{col 27}{res}{space 2} 1.162272{col 39}{space 2} 2.490204{col 50}{space 1}    0.07{col 59}{space 3}0.944{col 67}{space 4} .0174414{col 80}{space 3} 77.45222
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .9988586{col 39}{space 2} .0900031{col 50}{space 1}   -0.01{col 59}{space 3}0.990{col 67}{space 4} .8371547{col 80}{space 3} 1.191797
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .4607975{col 39}{space 2} .4805811{col 50}{space 1}   -0.74{col 59}{space 3}0.458{col 67}{space 4}  .059671{col 80}{space 3} 3.558416
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 5.08e-33{col 39}{space 2} 1.61e-31{col 67}{space 4} 6.01e-60{col 80}{space 3} 4.29e-06
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}14
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea10.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A10. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea10.rtf"'})

{com}. 
. *************
. **Table A11**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvol_close lpss_mod3_upd if p_niche==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-6936.7694}  
Iteration 1:{space 3}log likelihood = {res:-6599.1455}  
Iteration 2:{space 3}log likelihood = {res:-6597.3375}  
Iteration 3:{space 3}log likelihood = {res:-6597.3348}  
Iteration 4:{space 3}log likelihood = {res:-6597.3348}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6463.1018}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6463.1018}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6459.3733}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6443.1601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-6438.2762}  
Iteration 4:{space 3}log pseudolikelihood = {res:-6438.2638}  
Iteration 5:{space 3}log pseudolikelihood = {res:-6438.2638}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,087
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        35

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       149
{col 63}{txt}avg{col 67}={res}{col 69}     688.2
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   392.83
{txt}Log pseudolikelihood = {res}-6438.2638{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:35} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.099831{col 39}{space 2} .0720936{col 50}{space 1}    1.45{col 59}{space 3}0.147{col 67}{space 4} .9672302{col 80}{space 3}  1.25061
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9847406{col 39}{space 2} .0026201{col 50}{space 1}   -5.78{col 59}{space 3}0.000{col 67}{space 4} .9796186{col 80}{space 3} .9898894
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.291208{col 39}{space 2} .1301527{col 50}{space 1}    2.54{col 59}{space 3}0.011{col 67}{space 4} 1.059732{col 80}{space 3} 1.573246
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.386846{col 39}{space 2} .1870913{col 50}{space 1}    2.42{col 59}{space 3}0.015{col 67}{space 4} 1.064628{col 80}{space 3} 1.806587
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8021411{col 39}{space 2} .0557786{col 50}{space 1}   -3.17{col 59}{space 3}0.002{col 67}{space 4} .6999398{col 80}{space 3} .9192653
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6290487{col 39}{space 2} .0598004{col 50}{space 1}   -4.88{col 59}{space 3}0.000{col 67}{space 4} .5221136{col 80}{space 3} .7578855
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.118996{col 39}{space 2} .1836379{col 50}{space 1}    8.67{col 59}{space 3}0.000{col 67}{space 4}  1.78798{col 80}{space 3} 2.511294
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9880756{col 39}{space 2} .0349437{col 50}{space 1}   -0.34{col 59}{space 3}0.734{col 67}{space 4} .9219069{col 80}{space 3} 1.058993
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2}  .397927{col 39}{space 2} .0426441{col 50}{space 1}   -8.60{col 59}{space 3}0.000{col 67}{space 4} .3225402{col 80}{space 3} .4909338
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.200276{col 39}{space 2} .2477202{col 50}{space 1}    0.88{col 59}{space 3}0.376{col 67}{space 4} .8009493{col 80}{space 3} 1.798694
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.038683{col 39}{space 2} .0194228{col 50}{space 1}    2.03{col 59}{space 3}0.042{col 67}{space 4} 1.001305{col 80}{space 3} 1.077458
{txt}{space 15}lvol_close {c |}{col 27}{res}{space 2} .0809219{col 39}{space 2} .1496247{col 50}{space 1}   -1.36{col 59}{space 3}0.174{col 67}{space 4} .0021587{col 80}{space 3} 3.033466
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.334553{col 39}{space 2} .1240882{col 50}{space 1}    3.10{col 59}{space 3}0.002{col 67}{space 4} 1.112219{col 80}{space 3} 1.601333
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .2879424{col 39}{space 2} .2444412{col 50}{space 1}   -1.47{col 59}{space 3}0.142{col 67}{space 4} .0545381{col 80}{space 3} 1.520236
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4742583{col 39}{space 2} .2048456{col 67}{space 4} .2034028{col 80}{space 3} 1.105791
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}35
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvol_close lpss_mod3_upd if p_niche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2110.0594}  
Iteration 1:{space 3}log likelihood = {res: -2104.892}  
Iteration 2:{space 3}log likelihood = {res:-2104.8878}  
Iteration 3:{space 3}log likelihood = {res:-2104.8878}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2096.9823}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2096.9823}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2090.9962}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2089.2826}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2088.8927}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2088.8908}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2088.8908}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,173
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        28

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     149.0
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   220.53
{txt}Log pseudolikelihood = {res}-2088.8908{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:28} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8274054{col 39}{space 2} .0939296{col 50}{space 1}   -1.67{col 59}{space 3}0.095{col 67}{space 4} .6623498{col 80}{space 3} 1.033592
{txt}{space 22}age {c |}{col 27}{res}{space 2}  .992402{col 39}{space 2} .0029463{col 50}{space 1}   -2.57{col 59}{space 3}0.010{col 67}{space 4}  .986644{col 80}{space 3} .9981935
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.309002{col 39}{space 2} .1716926{col 50}{space 1}    2.05{col 59}{space 3}0.040{col 67}{space 4} 1.012265{col 80}{space 3} 1.692725
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.056343{col 39}{space 2} .1590583{col 50}{space 1}    0.36{col 59}{space 3}0.716{col 67}{space 4} .7863857{col 80}{space 3} 1.418973
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.115826{col 39}{space 2} .1140249{col 50}{space 1}    1.07{col 59}{space 3}0.284{col 67}{space 4} .9132998{col 80}{space 3} 1.363264
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.608643{col 39}{space 2} .1978435{col 50}{space 1}    3.87{col 59}{space 3}0.000{col 67}{space 4} 1.264073{col 80}{space 3} 2.047138
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .8016728{col 39}{space 2} .0713438{col 50}{space 1}   -2.48{col 59}{space 3}0.013{col 67}{space 4} .6733574{col 80}{space 3} .9544401
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.111411{col 39}{space 2} .0426741{col 50}{space 1}    2.75{col 59}{space 3}0.006{col 67}{space 4} 1.030841{col 80}{space 3} 1.198278
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4243879{col 39}{space 2} .0429334{col 50}{space 1}   -8.47{col 59}{space 3}0.000{col 67}{space 4} .3480574{col 80}{space 3} .5174581
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7471373{col 39}{space 2} .1650221{col 50}{space 1}   -1.32{col 59}{space 3}0.187{col 67}{space 4} .4846112{col 80}{space 3}  1.15188
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9799096{col 39}{space 2} .0134032{col 50}{space 1}   -1.48{col 59}{space 3}0.138{col 67}{space 4} .9539889{col 80}{space 3} 1.006535
{txt}{space 15}lvol_close {c |}{col 27}{res}{space 2} 2.230715{col 39}{space 2} 4.177528{col 50}{space 1}    0.43{col 59}{space 3}0.668{col 67}{space 4} .0568033{col 80}{space 3} 87.60221
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .8606609{col 39}{space 2} .0404974{col 50}{space 1}   -3.19{col 59}{space 3}0.001{col 67}{space 4} .7848376{col 80}{space 3} .9438095
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .4859175{col 39}{space 2} .5149403{col 50}{space 1}   -0.68{col 59}{space 3}0.496{col 67}{space 4} .0608864{col 80}{space 3}  3.87797
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1966229{col 39}{space 2} .1850587{col 67}{space 4}  .031081{col 80}{space 3} 1.243864
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}28
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvol_close lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5155.2926}  
Iteration 1:{space 3}log likelihood = {res:-4542.8179}  
Iteration 2:{space 3}log likelihood = {res:-4522.8352}  
Iteration 3:{space 3}log likelihood = {res:-4522.6279}  
Iteration 4:{space 3}log likelihood = {res:-4522.6279}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4232.9115}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4232.9115}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4196.5228}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4193.4129}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4193.0745}  
Iteration 4:{space 3}log pseudolikelihood = {res:-4193.0609}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4193.0608}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    23,295
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        35

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       134
{col 63}{txt}avg{col 67}={res}{col 69}     665.6
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   823.15
{txt}Log pseudolikelihood = {res}-4193.0608{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:35} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}c_radicalrl_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.335013{col 39}{space 2} .0948997{col 50}{space 1}    4.06{col 59}{space 3}0.000{col 67}{space 4} 1.161389{col 80}{space 3} 1.534594
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9879748{col 39}{space 2} .0030843{col 50}{space 1}   -3.88{col 59}{space 3}0.000{col 67}{space 4} .9819482{col 80}{space 3} .9940384
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.394416{col 39}{space 2} .2025571{col 50}{space 1}    2.29{col 59}{space 3}0.022{col 67}{space 4} 1.048924{col 80}{space 3} 1.853704
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  1.18329{col 39}{space 2} .2012241{col 50}{space 1}    0.99{col 59}{space 3}0.322{col 67}{space 4} .8478917{col 80}{space 3} 1.651361
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.726543{col 39}{space 2} .2698281{col 50}{space 1}   10.14{col 59}{space 3}0.000{col 67}{space 4} 2.245818{col 80}{space 3} 3.310169
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .7262662{col 39}{space 2} .0606396{col 50}{space 1}   -3.83{col 59}{space 3}0.000{col 67}{space 4} .6166302{col 80}{space 3} .8553954
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .5408388{col 39}{space 2} .0529439{col 50}{space 1}   -6.28{col 59}{space 3}0.000{col 67}{space 4} .4464182{col 80}{space 3} .6552301
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.053694{col 39}{space 2} .0435254{col 50}{space 1}    1.27{col 59}{space 3}0.205{col 67}{space 4} .9717477{col 80}{space 3} 1.142551
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3523645{col 39}{space 2} .0473791{col 50}{space 1}   -7.76{col 59}{space 3}0.000{col 67}{space 4} .2707318{col 80}{space 3} .4586117
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.112885{col 39}{space 2} .2613093{col 50}{space 1}    0.46{col 59}{space 3}0.649{col 67}{space 4} .7024013{col 80}{space 3} 1.763256
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.034442{col 39}{space 2} .0346203{col 50}{space 1}    1.01{col 59}{space 3}0.312{col 67}{space 4} .9687649{col 80}{space 3} 1.104571
{txt}{space 15}lvol_close {c |}{col 27}{res}{space 2} .0001596{col 39}{space 2} .0007175{col 50}{space 1}   -1.95{col 59}{space 3}0.052{col 67}{space 4} 2.39e-08{col 80}{space 3} 1.068474
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 3.587929{col 39}{space 2} 1.174734{col 50}{space 1}    3.90{col 59}{space 3}0.000{col 67}{space 4} 1.888641{col 80}{space 3} 6.816135
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 1.259313{col 39}{space 2} 2.401024{col 50}{space 1}    0.12{col 59}{space 3}0.904{col 67}{space 4} .0300069{col 80}{space 3} 52.85021
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 2.436292{col 39}{space 2} 1.210552{col 67}{space 4} .9199868{col 80}{space 3} 6.451742
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}35
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvol_close lpss_mod3_upd if p_radicalrl_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1245.4649}  
Iteration 1:{space 3}log likelihood = {res:-1241.0992}  
Iteration 2:{space 3}log likelihood = {res:-1241.0886}  
Iteration 3:{space 3}log likelihood = {res:-1241.0886}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1218.5408}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1218.5408}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1217.2451}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1214.2222}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1214.0282}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1214.0244}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1214.0244}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,521
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        23

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     109.6
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   472.58
{txt}Log pseudolikelihood = {res}-1214.0244{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:23} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}c_mainstream_vs_radicalrl{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .894407{col 39}{space 2} .1425677{col 50}{space 1}   -0.70{col 59}{space 3}0.484{col 67}{space 4} .6544168{col 80}{space 3} 1.222407
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9919421{col 39}{space 2} .0039063{col 50}{space 1}   -2.05{col 59}{space 3}0.040{col 67}{space 4} .9843153{col 80}{space 3}  .999628
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.202057{col 39}{space 2} .2284543{col 50}{space 1}    0.97{col 59}{space 3}0.333{col 67}{space 4} .8282319{col 80}{space 3}  1.74461
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  .763963{col 39}{space 2} .1702556{col 50}{space 1}   -1.21{col 59}{space 3}0.227{col 67}{space 4} .4935997{col 80}{space 3} 1.182414
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .6689938{col 39}{space 2} .0557071{col 50}{space 1}   -4.83{col 59}{space 3}0.000{col 67}{space 4} .5682542{col 80}{space 3} .7875925
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.132737{col 39}{space 2} .1927484{col 50}{space 1}    0.73{col 59}{space 3}0.464{col 67}{space 4} .8114977{col 80}{space 3} 1.581142
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.811144{col 39}{space 2} .2983039{col 50}{space 1}    3.61{col 59}{space 3}0.000{col 67}{space 4} 1.311463{col 80}{space 3} 2.501208
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.045979{col 39}{space 2} .0531998{col 50}{space 1}    0.88{col 59}{space 3}0.377{col 67}{space 4} .9467375{col 80}{space 3} 1.155622
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4015723{col 39}{space 2}  .045311{col 50}{space 1}   -8.09{col 59}{space 3}0.000{col 67}{space 4} .3218988{col 80}{space 3} .5009659
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.661033{col 39}{space 2} .2703262{col 50}{space 1}    3.12{col 59}{space 3}0.002{col 67}{space 4} 1.207393{col 80}{space 3} 2.285113
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9533817{col 39}{space 2} .0194744{col 50}{space 1}   -2.34{col 59}{space 3}0.019{col 67}{space 4} .9159665{col 80}{space 3} .9923252
{txt}{space 15}lvol_close {c |}{col 27}{res}{space 2} 5.389156{col 39}{space 2} 16.32324{col 50}{space 1}    0.56{col 59}{space 3}0.578{col 67}{space 4} .0142335{col 80}{space 3} 2040.468
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .8222918{col 39}{space 2}   .15448{col 50}{space 1}   -1.04{col 59}{space 3}0.298{col 67}{space 4} .5690034{col 80}{space 3}  1.18833
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .5737444{col 39}{space 2} .9254037{col 50}{space 1}   -0.34{col 59}{space 3}0.731{col 67}{space 4} .0243108{col 80}{space 3} 13.54061
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4723926{col 39}{space 2} .2377397{col 67}{space 4} .1761657{col 80}{space 3} 1.266732
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}23
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvol_close lpss_mod3_upd if p_green_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3328.2598}  
Iteration 1:{space 3}log likelihood = {res:-2337.0614}  
Iteration 2:{space 3}log likelihood = {res:-2288.2081}  
Iteration 3:{space 3}log likelihood = {res:-2283.9376}  
Iteration 4:{space 3}log likelihood = {res:-2283.8773}  
Iteration 5:{space 3}log likelihood = {res:-2283.8772}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2138.1857}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2138.1857}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2118.0138}  
Iteration 2:{space 3}log pseudolikelihood = {res: -2112.206}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2110.5308}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2110.2447}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2110.2297}  
Iteration 6:{space 3}log pseudolikelihood = {res:-2110.2294}  
Iteration 7:{space 3}log pseudolikelihood = {res:-2110.2293}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    22,549
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        35

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       134
{col 63}{txt}avg{col 67}={res}{col 69}     644.3
{col 63}{txt}max{col 67}={res}{col 69}     1,256

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   483.57
{txt}Log pseudolikelihood = {res}-2110.2293{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:35} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .6789581{col 39}{space 2} .0527316{col 50}{space 1}   -4.99{col 59}{space 3}0.000{col 67}{space 4} .5830879{col 80}{space 3} .7905911
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9785088{col 39}{space 2} .0038071{col 50}{space 1}   -5.58{col 59}{space 3}0.000{col 67}{space 4} .9710754{col 80}{space 3}  .985999
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.867647{col 39}{space 2} .5813139{col 50}{space 1}    2.01{col 59}{space 3}0.045{col 67}{space 4} 1.014739{col 80}{space 3} 3.437441
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  3.50605{col 39}{space 2} 1.416728{col 50}{space 1}    3.10{col 59}{space 3}0.002{col 67}{space 4} 1.588048{col 80}{space 3}  7.74056
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.135776{col 39}{space 2} .1613786{col 50}{space 1}    0.90{col 59}{space 3}0.370{col 67}{space 4} .8597029{col 80}{space 3} 1.500504
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.051748{col 39}{space 2} .1447549{col 50}{space 1}    0.37{col 59}{space 3}0.714{col 67}{space 4} .8030796{col 80}{space 3} 1.377415
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9133008{col 39}{space 2} .1967818{col 50}{space 1}   -0.42{col 59}{space 3}0.674{col 67}{space 4} .5987039{col 80}{space 3} 1.393207
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9284323{col 39}{space 2}  .041211{col 50}{space 1}   -1.67{col 59}{space 3}0.094{col 67}{space 4} .8510741{col 80}{space 3} 1.012822
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5586896{col 39}{space 2} .0589191{col 50}{space 1}   -5.52{col 59}{space 3}0.000{col 67}{space 4} .4543634{col 80}{space 3} .6869701
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.619506{col 39}{space 2} .4786053{col 50}{space 1}    1.63{col 59}{space 3}0.103{col 67}{space 4} .9074668{col 80}{space 3} 2.890243
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.112041{col 39}{space 2} .0461819{col 50}{space 1}    2.56{col 59}{space 3}0.011{col 67}{space 4} 1.025112{col 80}{space 3} 1.206342
{txt}{space 15}lvol_close {c |}{col 27}{res}{space 2} 1.27e-06{col 39}{space 2} 5.42e-06{col 50}{space 1}   -3.18{col 59}{space 3}0.001{col 67}{space 4} 2.91e-10{col 80}{space 3} .0055148
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .7629101{col 39}{space 2} .2585125{col 50}{space 1}   -0.80{col 59}{space 3}0.425{col 67}{space 4}  .392684{col 80}{space 3} 1.482189
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .5887929{col 39}{space 2} 1.081126{col 50}{space 1}   -0.29{col 59}{space 3}0.773{col 67}{space 4} .0161068{col 80}{space 3} 21.52367
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 6.053256{col 39}{space 2} 2.483688{col 67}{space 4} 2.708544{col 80}{space 3} 13.52827
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}35
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvol_close lpss_mod3_upd if p_green_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-544.52406}  
Iteration 1:{space 3}log likelihood = {res:-543.93603}  
Iteration 2:{space 3}log likelihood = {res:-543.93589}  
Iteration 3:{space 3}log likelihood = {res:-543.93589}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-553.47013}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-553.47013}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-550.02985}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-546.54591}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-545.01792}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-544.39801}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-544.14217}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res:-544.03758}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-544.01656}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-544.00813}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res:-544.00475}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res:-544.00407}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res:-544.00394}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res:-544.00389}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res:-544.00388}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res:-544.00388}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res:-544.00388}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-544.00388}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-544.00388}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-544.00388}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 20:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 21:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 22:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-544.00388}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-544.00388}  (backed up)
Iteration 31:{space 2}log pseudolikelihood = {res:-544.00388}  (not concave)
Iteration 32:{space 2}log pseudolikelihood = {res:-544.00388}  
Iteration 33:{space 2}log pseudolikelihood = {res:-544.00387}  (backed up)
Iteration 34:{space 2}log pseudolikelihood = {res:-544.00386}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-544.00385}  (not concave)
Iteration 36:{space 2}log pseudolikelihood = {res:-544.00384}  
Iteration 37:{space 2}log pseudolikelihood = {res:-544.00371}  (not concave)
Iteration 38:{space 2}log pseudolikelihood = {res: -544.0037}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res:-544.00368}  
Iteration 40:{space 2}log pseudolikelihood = {res:-544.00263}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res:-543.99854}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res:-543.99851}  (not concave)
Iteration 43:{space 2}log pseudolikelihood = {res:-543.99713}  
Iteration 44:{space 2}log pseudolikelihood = {res: -543.9359}  
Iteration 45:{space 2}log pseudolikelihood = {res:-543.93589}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-543.93589}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       967
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        18

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         6
{col 63}{txt}avg{col 67}={res}{col 69}      53.7
{col 63}{txt}max{col 67}={res}{col 69}       177

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   207.44
{txt}Log pseudolikelihood = {res}-543.93589{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:18} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8056127{col 39}{space 2} .1199337{col 50}{space 1}   -1.45{col 59}{space 3}0.147{col 67}{space 4} .6017356{col 80}{space 3} 1.078567
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9993724{col 39}{space 2} .0060688{col 50}{space 1}   -0.10{col 59}{space 3}0.918{col 67}{space 4} .9875483{col 80}{space 3} 1.011338
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .9508775{col 39}{space 2} .3294889{col 50}{space 1}   -0.15{col 59}{space 3}0.884{col 67}{space 4} .4821414{col 80}{space 3} 1.875317
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8345753{col 39}{space 2} .3267675{col 50}{space 1}   -0.46{col 59}{space 3}0.644{col 67}{space 4} .3874255{col 80}{space 3} 1.797806
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.245135{col 39}{space 2} .2104173{col 50}{space 1}    1.30{col 59}{space 3}0.195{col 67}{space 4} .8940682{col 80}{space 3} 1.734053
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9057403{col 39}{space 2} .1321242{col 50}{space 1}   -0.68{col 59}{space 3}0.497{col 67}{space 4} .6805113{col 80}{space 3} 1.205513
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9645937{col 39}{space 2}  .193348{col 50}{space 1}   -0.18{col 59}{space 3}0.857{col 67}{space 4} .6512164{col 80}{space 3} 1.428774
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.322241{col 39}{space 2} .0878987{col 50}{space 1}    4.20{col 59}{space 3}0.000{col 67}{space 4} 1.160714{col 80}{space 3} 1.506246
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4920097{col 39}{space 2} .0921574{col 50}{space 1}   -3.79{col 59}{space 3}0.000{col 67}{space 4} .3408292{col 80}{space 3} .7102489
{txt}{space 13}p_government {c |}{col 27}{res}{space 2}  .755504{col 39}{space 2}  .210661{col 50}{space 1}   -1.01{col 59}{space 3}0.315{col 67}{space 4} .4374132{col 80}{space 3} 1.304913
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9858508{col 39}{space 2}  .010579{col 50}{space 1}   -1.33{col 59}{space 3}0.184{col 67}{space 4} .9653328{col 80}{space 3} 1.006805
{txt}{space 15}lvol_close {c |}{col 27}{res}{space 2} 2.170206{col 39}{space 2} 2.757275{col 50}{space 1}    0.61{col 59}{space 3}0.542{col 67}{space 4}  .179903{col 80}{space 3} 26.17963
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2}   .97044{col 39}{space 2} .0491974{col 50}{space 1}   -0.59{col 59}{space 3}0.554{col 67}{space 4} .8786507{col 80}{space 3} 1.071818
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .4241308{col 39}{space 2}   .31162{col 50}{space 1}   -1.17{col 59}{space 3}0.243{col 67}{space 4} .1004847{col 80}{space 3} 1.790193
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.37e-37{col 39}{space 2} 2.74e-37{col 67}{space 4} 2.73e-39{col 80}{space 3} 6.89e-36
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}18
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea11.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A11. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea11.rtf"'})

{com}. 
. *************
. **Table A12**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined gparties llsq lpss_mod3_upd if p_niche==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-7181.4849}  
Iteration 1:{space 3}log likelihood = {res:-6825.7608}  
Iteration 2:{space 3}log likelihood = {res:-6824.2424}  
Iteration 3:{space 3}log likelihood = {res:-6824.2406}  
Iteration 4:{space 3}log likelihood = {res:-6824.2406}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6665.9239}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6665.9239}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6662.0116}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6644.3298}  
Iteration 3:{space 3}log pseudolikelihood = {res:-6638.8318}  
Iteration 4:{space 3}log pseudolikelihood = {res:-6638.7033}  
Iteration 5:{space 3}log pseudolikelihood = {res: -6638.702}  
Iteration 6:{space 3}log pseudolikelihood = {res: -6638.702}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,211
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        38

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}     663.4
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   476.33
{txt}Log pseudolikelihood = {res}-6638.702{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:38} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.077558{col 39}{space 2} .0696983{col 50}{space 1}    1.15{col 59}{space 3}0.248{col 67}{space 4}  .949256{col 80}{space 3} 1.223201
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9847906{col 39}{space 2} .0025623{col 50}{space 1}   -5.89{col 59}{space 3}0.000{col 67}{space 4} .9797814{col 80}{space 3} .9898253
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.273373{col 39}{space 2}  .122445{col 50}{space 1}    2.51{col 59}{space 3}0.012{col 67}{space 4} 1.054644{col 80}{space 3} 1.537465
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.377735{col 39}{space 2}  .176959{col 50}{space 1}    2.49{col 59}{space 3}0.013{col 67}{space 4} 1.071114{col 80}{space 3} 1.772131
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8259722{col 39}{space 2}  .056219{col 50}{space 1}   -2.81{col 59}{space 3}0.005{col 67}{space 4} .7228184{col 80}{space 3}  .943847
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6482776{col 39}{space 2} .0610991{col 50}{space 1}   -4.60{col 59}{space 3}0.000{col 67}{space 4} .5389353{col 80}{space 3} .7798038
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.114864{col 39}{space 2} .1791668{col 50}{space 1}    8.84{col 59}{space 3}0.000{col 67}{space 4} 1.791309{col 80}{space 3} 2.496862
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2}  .977486{col 39}{space 2} .0342551{col 50}{space 1}   -0.65{col 59}{space 3}0.516{col 67}{space 4}  .912601{col 80}{space 3} 1.046984
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3946377{col 39}{space 2} .0414163{col 50}{space 1}   -8.86{col 59}{space 3}0.000{col 67}{space 4} .3212676{col 80}{space 3} .4847637
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.177778{col 39}{space 2} .2332987{col 50}{space 1}    0.83{col 59}{space 3}0.409{col 67}{space 4} .7988296{col 80}{space 3} 1.736491
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.013733{col 39}{space 2} .0134241{col 50}{space 1}    1.03{col 59}{space 3}0.303{col 67}{space 4} .9877609{col 80}{space 3} 1.040388
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} 1.023083{col 39}{space 2} .0098816{col 50}{space 1}    2.36{col 59}{space 3}0.018{col 67}{space 4} 1.003897{col 80}{space 3} 1.042635
{txt}{space 17}gparties {c |}{col 27}{res}{space 2} 1.053549{col 39}{space 2} .1233746{col 50}{space 1}    0.45{col 59}{space 3}0.656{col 67}{space 4} .8374827{col 80}{space 3} 1.325359
{txt}{space 21}llsq {c |}{col 27}{res}{space 2} .9807598{col 39}{space 2} .0580428{col 50}{space 1}   -0.33{col 59}{space 3}0.743{col 67}{space 4} .8733479{col 80}{space 3} 1.101382
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.137653{col 39}{space 2}  .103204{col 50}{space 1}    1.42{col 59}{space 3}0.155{col 67}{space 4} .9523395{col 80}{space 3} 1.359027
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0829049{col 39}{space 2} .0355178{col 50}{space 1}   -5.81{col 59}{space 3}0.000{col 67}{space 4} .0358024{col 80}{space 3} .1919767
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4513882{col 39}{space 2} .1665669{col 67}{space 4} .2190019{col 80}{space 3}  .930363
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}38
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined gparties llsq lpss_mod3_upd if p_niche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2193.8422}  
Iteration 1:{space 3}log likelihood = {res:-2188.2103}  
Iteration 2:{space 3}log likelihood = {res: -2188.205}  
Iteration 3:{space 3}log likelihood = {res: -2188.205}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2178.4139}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2178.4139}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2171.8891}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2170.1585}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2169.0687}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2169.0258}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2169.0256}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,408
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        31

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     142.2
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   253.49
{txt}Log pseudolikelihood = {res}-2169.0256{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:31} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8029294{col 39}{space 2} .0873612{col 50}{space 1}   -2.02{col 59}{space 3}0.044{col 67}{space 4}   .64873{col 80}{space 3} .9937811
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9936829{col 39}{space 2} .0028154{col 50}{space 1}   -2.24{col 59}{space 3}0.025{col 67}{space 4} .9881801{col 80}{space 3} .9992163
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.292147{col 39}{space 2} .1544065{col 50}{space 1}    2.14{col 59}{space 3}0.032{col 67}{space 4} 1.022343{col 80}{space 3} 1.633154
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.046887{col 39}{space 2} .1473448{col 50}{space 1}    0.33{col 59}{space 3}0.745{col 67}{space 4} .7945055{col 80}{space 3}  1.37944
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.125416{col 39}{space 2} .1204562{col 50}{space 1}    1.10{col 59}{space 3}0.270{col 67}{space 4} .9124449{col 80}{space 3} 1.388095
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.623439{col 39}{space 2} .1972141{col 50}{space 1}    3.99{col 59}{space 3}0.000{col 67}{space 4} 1.279478{col 80}{space 3} 2.059868
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .7728634{col 39}{space 2} .0653401{col 50}{space 1}   -3.05{col 59}{space 3}0.002{col 67}{space 4} .6548467{col 80}{space 3}  .912149
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.109183{col 39}{space 2} .0408597{col 50}{space 1}    2.81{col 59}{space 3}0.005{col 67}{space 4} 1.031922{col 80}{space 3} 1.192228
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4293879{col 39}{space 2} .0424598{col 50}{space 1}   -8.55{col 59}{space 3}0.000{col 67}{space 4} .3537359{col 80}{space 3} .5212193
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7478309{col 39}{space 2} .1465507{col 50}{space 1}   -1.48{col 59}{space 3}0.138{col 67}{space 4} .5093255{col 80}{space 3} 1.098023
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9961764{col 39}{space 2} .0128628{col 50}{space 1}   -0.30{col 59}{space 3}0.767{col 67}{space 4}  .971282{col 80}{space 3} 1.021709
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} 1.002577{col 39}{space 2} .0076708{col 50}{space 1}    0.34{col 59}{space 3}0.737{col 67}{space 4} .9876552{col 80}{space 3} 1.017725
{txt}{space 17}gparties {c |}{col 27}{res}{space 2} .9383966{col 39}{space 2} .0690701{col 50}{space 1}   -0.86{col 59}{space 3}0.388{col 67}{space 4} .8123334{col 80}{space 3} 1.084023
{txt}{space 21}llsq {c |}{col 27}{res}{space 2} 1.062469{col 39}{space 2}  .032172{col 50}{space 1}    2.00{col 59}{space 3}0.045{col 67}{space 4} 1.001248{col 80}{space 3} 1.127434
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .8992281{col 39}{space 2} .0526623{col 50}{space 1}   -1.81{col 59}{space 3}0.070{col 67}{space 4} .8017155{col 80}{space 3} 1.008601
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .4341886{col 39}{space 2} .1683794{col 50}{space 1}   -2.15{col 59}{space 3}0.031{col 67}{space 4} .2030394{col 80}{space 3} .9284885
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1709933{col 39}{space 2} .1272796{col 67}{space 4} .0397542{col 80}{space 3} .7354866
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}31
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined gparties llsq lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5330.0176}  
Iteration 1:{space 3}log likelihood = {res:-4684.7175}  
Iteration 2:{space 3}log likelihood = {res:-4673.9001}  
Iteration 3:{space 3}log likelihood = {res:-4673.8423}  
Iteration 4:{space 3}log likelihood = {res:-4673.8423}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4370.2447}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4370.2447}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4341.2928}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4330.2313}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4328.9479}  
Iteration 4:{space 3}log pseudolikelihood = {res:-4328.8546}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4328.8537}  
Iteration 6:{space 3}log pseudolikelihood = {res:-4328.8537}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,397
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        38

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     642.0
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   858.36
{txt}Log pseudolikelihood = {res}-4328.8537{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:38} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.294771{col 40}{space 2} .0897439{col 51}{space 1}    3.73{col 60}{space 3}0.000{col 68}{space 4} 1.130301{col 81}{space 3} 1.483174
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9876756{col 40}{space 2} .0030487{col 51}{space 1}   -4.02{col 60}{space 3}0.000{col 68}{space 4} .9817182{col 81}{space 3} .9936691
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2}  1.38385{col 40}{space 2} .1922357{col 51}{space 1}    2.34{col 60}{space 3}0.019{col 68}{space 4} 1.054012{col 81}{space 3} 1.816906
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.173041{col 40}{space 2} .1916105{col 51}{space 1}    0.98{col 60}{space 3}0.329{col 68}{space 4}  .851674{col 81}{space 3}  1.61567
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.724758{col 40}{space 2} .2669962{col 51}{space 1}   10.23{col 60}{space 3}0.000{col 68}{space 4} 2.248638{col 81}{space 3}  3.30169
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2}  .747585{col 40}{space 2} .0613783{col 51}{space 1}   -3.54{col 60}{space 3}0.000{col 68}{space 4} .6364659{col 81}{space 3} .8781043
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5468985{col 40}{space 2}  .053294{col 51}{space 1}   -6.19{col 60}{space 3}0.000{col 68}{space 4} .4518135{col 81}{space 3} .6619943
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.042314{col 40}{space 2} .0421014{col 51}{space 1}    1.03{col 60}{space 3}0.305{col 68}{space 4} .9629789{col 81}{space 3} 1.128186
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3507767{col 40}{space 2} .0463814{col 51}{space 1}   -7.92{col 60}{space 3}0.000{col 68}{space 4} .2706953{col 81}{space 3}  .454549
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.114332{col 40}{space 2} .2531039{col 51}{space 1}    0.48{col 60}{space 3}0.634{col 68}{space 4} .7139653{col 81}{space 3} 1.739212
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9575825{col 40}{space 2} .0310146{col 51}{space 1}   -1.34{col 60}{space 3}0.181{col 68}{space 4} .8986842{col 81}{space 3} 1.020341
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 1.088061{col 40}{space 2} .0417667{col 51}{space 1}    2.20{col 60}{space 3}0.028{col 68}{space 4} 1.009203{col 81}{space 3}  1.17308
{txt}{space 18}gparties {c |}{col 28}{res}{space 2} 1.149116{col 40}{space 2} .2680686{col 51}{space 1}    0.60{col 60}{space 3}0.551{col 68}{space 4} .7274339{col 81}{space 3} 1.815241
{txt}{space 22}llsq {c |}{col 28}{res}{space 2} 1.001394{col 40}{space 2} .0976155{col 51}{space 1}    0.01{col 60}{space 3}0.989{col 68}{space 4} .8272372{col 81}{space 3} 1.212215
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} 1.811635{col 40}{space 2}  .444147{col 51}{space 1}    2.42{col 60}{space 3}0.015{col 68}{space 4} 1.120436{col 81}{space 3} 2.929236
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0253643{col 40}{space 2} .0212909{col 51}{space 1}   -4.38{col 60}{space 3}0.000{col 68}{space 4} .0048947{col 81}{space 3} .1314391
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 2.063884{col 40}{space 2}  .886629{col 68}{space 4} .8892332{col 81}{space 3} 4.790215
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}38
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined gparties llsq lpss_mod3_upd if p_radicalrl_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1276.7738}  
Iteration 1:{space 3}log likelihood = {res:-1271.2239}  
Iteration 2:{space 3}log likelihood = {res:-1271.2033}  
Iteration 3:{space 3}log likelihood = {res:-1271.2033}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1250.9353}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1250.9353}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1248.2632}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1245.3935}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1245.1403}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1245.1381}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1245.1381}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,657
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        26

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     102.2
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   557.16
{txt}Log pseudolikelihood = {res}-1245.1381{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:26} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8831163{col 40}{space 2} .1385638{col 51}{space 1}   -0.79{col 60}{space 3}0.428{col 68}{space 4} .6493243{col 81}{space 3} 1.201086
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9941156{col 40}{space 2} .0036971{col 51}{space 1}   -1.59{col 60}{space 3}0.113{col 68}{space 4} .9868958{col 81}{space 3} 1.001388
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2}  1.14356{col 40}{space 2} .1947189{col 51}{space 1}    0.79{col 60}{space 3}0.431{col 68}{space 4} .8190704{col 81}{space 3} 1.596602
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7307436{col 40}{space 2} .1494289{col 51}{space 1}   -1.53{col 60}{space 3}0.125{col 68}{space 4} .4894449{col 81}{space 3} 1.091004
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .6517056{col 40}{space 2} .0525474{col 51}{space 1}   -5.31{col 60}{space 3}0.000{col 68}{space 4} .5564404{col 81}{space 3} .7632807
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.142301{col 40}{space 2} .1924276{col 51}{space 1}    0.79{col 60}{space 3}0.430{col 68}{space 4}  .821089{col 81}{space 3} 1.589171
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.826444{col 40}{space 2}  .289611{col 51}{space 1}    3.80{col 60}{space 3}0.000{col 68}{space 4} 1.338552{col 81}{space 3}  2.49217
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.048778{col 40}{space 2} .0515938{col 51}{space 1}    0.97{col 60}{space 3}0.333{col 68}{space 4} .9523785{col 81}{space 3} 1.154936
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3866364{col 40}{space 2} .0442298{col 51}{space 1}   -8.31{col 60}{space 3}0.000{col 68}{space 4} .3089786{col 81}{space 3} .4838125
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.371835{col 40}{space 2} .4180833{col 51}{space 1}    1.04{col 60}{space 3}0.300{col 68}{space 4} .7548973{col 81}{space 3} 2.492963
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9823342{col 40}{space 2} .0206129{col 51}{space 1}   -0.85{col 60}{space 3}0.396{col 68}{space 4} .9427533{col 81}{space 3} 1.023577
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9965533{col 40}{space 2} .0195793{col 51}{space 1}   -0.18{col 60}{space 3}0.861{col 68}{space 4} .9589082{col 81}{space 3} 1.035676
{txt}{space 18}gparties {c |}{col 28}{res}{space 2} .8661229{col 40}{space 2}  .109891{col 51}{space 1}   -1.13{col 60}{space 3}0.257{col 68}{space 4} .6754321{col 81}{space 3}  1.11065
{txt}{space 22}llsq {c |}{col 28}{res}{space 2} 1.028384{col 40}{space 2} .0376234{col 51}{space 1}    0.77{col 60}{space 3}0.444{col 68}{space 4} .9572256{col 81}{space 3} 1.104833
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} .9951373{col 40}{space 2}   .15533{col 51}{space 1}   -0.03{col 60}{space 3}0.975{col 68}{space 4}  .732858{col 81}{space 3} 1.351283
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .8585104{col 40}{space 2} .4991321{col 51}{space 1}   -0.26{col 60}{space 3}0.793{col 68}{space 4} .2747026{col 81}{space 3} 2.683047
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .3857429{col 40}{space 2} .1586879{col 68}{space 4} .1722378{col 81}{space 3} .8639078
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}26
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined gparties llsq lpss_mod3_upd if p_green_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -3440.752}  
Iteration 1:{space 3}log likelihood = {res:-2321.0229}  
Iteration 2:{space 3}log likelihood = {res:-2237.0525}  
Iteration 3:{space 3}log likelihood = {res:-2227.4362}  
Iteration 4:{space 3}log likelihood = {res: -2227.319}  
Iteration 5:{space 3}log likelihood = {res:-2227.3189}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2182.6377}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2182.6377}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2177.2314}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2172.2204}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2171.5613}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2171.5116}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2171.5113}  
Iteration 6:{space 3}log pseudolikelihood = {res:-2171.5113}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    23,635
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        38

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        70
{col 63}{txt}avg{col 67}={res}{col 69}     622.0
{col 63}{txt}max{col 67}={res}{col 69}     1,256

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   412.84
{txt}Log pseudolikelihood = {res}-2171.5113{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:38} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .6807556{col 39}{space 2} .0519459{col 50}{space 1}   -5.04{col 59}{space 3}0.000{col 67}{space 4} .5861911{col 80}{space 3} .7905752
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9791104{col 39}{space 2} .0037778{col 50}{space 1}   -5.47{col 59}{space 3}0.000{col 67}{space 4}  .971734{col 80}{space 3} .9865429
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.634072{col 39}{space 2} .4127623{col 50}{space 1}    1.94{col 59}{space 3}0.052{col 67}{space 4} .9960011{col 80}{space 3} 2.680911
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 3.100381{col 39}{space 2} 1.029963{col 50}{space 1}    3.41{col 59}{space 3}0.001{col 67}{space 4} 1.616745{col 80}{space 3} 5.945503
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.157796{col 39}{space 2} .1581336{col 50}{space 1}    1.07{col 59}{space 3}0.283{col 67}{space 4} .8858775{col 80}{space 3}  1.51318
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.080453{col 39}{space 2} .1426076{col 50}{space 1}    0.59{col 59}{space 3}0.558{col 67}{space 4} .8341747{col 80}{space 3} 1.399442
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9615471{col 39}{space 2} .1992967{col 50}{space 1}   -0.19{col 59}{space 3}0.850{col 67}{space 4}  .640538{col 80}{space 3} 1.443432
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2}  .915498{col 39}{space 2} .0431627{col 50}{space 1}   -1.87{col 59}{space 3}0.061{col 67}{space 4} .8346918{col 80}{space 3} 1.004127
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5419544{col 39}{space 2} .0579513{col 50}{space 1}   -5.73{col 59}{space 3}0.000{col 67}{space 4} .4394844{col 80}{space 3} .6683162
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.448216{col 39}{space 2} .4274307{col 50}{space 1}    1.25{col 59}{space 3}0.210{col 67}{space 4} .8120962{col 80}{space 3} 2.582614
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9860497{col 39}{space 2} .0326502{col 50}{space 1}   -0.42{col 59}{space 3}0.671{col 67}{space 4} .9240889{col 80}{space 3} 1.052165
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.798766{col 39}{space 2} .1727416{col 50}{space 1}    6.11{col 59}{space 3}0.000{col 67}{space 4} 1.490154{col 80}{space 3} 2.171293
{txt}{space 17}gparties {c |}{col 27}{res}{space 2} 1.275796{col 39}{space 2} .3290294{col 50}{space 1}    0.94{col 59}{space 3}0.345{col 67}{space 4} .7695822{col 80}{space 3} 2.114984
{txt}{space 21}llsq {c |}{col 27}{res}{space 2} 1.230112{col 39}{space 2}  .102231{col 50}{space 1}    2.49{col 59}{space 3}0.013{col 67}{space 4} 1.045211{col 80}{space 3} 1.447723
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2}  .772543{col 39}{space 2} .1575392{col 50}{space 1}   -1.27{col 59}{space 3}0.206{col 67}{space 4}  .518016{col 80}{space 3} 1.152132
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0002936{col 39}{space 2} .0003401{col 50}{space 1}   -7.02{col 59}{space 3}0.000{col 67}{space 4} .0000303{col 80}{space 3} .0028424
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.383322{col 39}{space 2} .5501469{col 67}{space 4}  .634455{col 80}{space 3} 3.016102
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}38
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined gparties llsq lpss_mod3_upd if p_green_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-575.96339}  
Iteration 1:{space 3}log likelihood = {res:-575.25254}  
Iteration 2:{space 3}log likelihood = {res:-575.25228}  
Iteration 3:{space 3}log likelihood = {res:-575.25228}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-585.40049}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-585.40049}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-575.51428}  
Iteration 2:{space 3}log pseudolikelihood = {res:-575.31064}  
Iteration 3:{space 3}log pseudolikelihood = {res:-575.30467}  (backed up)
Iteration 4:{space 3}log pseudolikelihood = {res:-575.30394}  (backed up)
Iteration 5:{space 3}log pseudolikelihood = {res:-575.30358}  (backed up)
Iteration 6:{space 3}log pseudolikelihood = {res: -575.3034}  (backed up)
Iteration 7:{space 3}log pseudolikelihood = {res:-575.30335}  (backed up)
Iteration 8:{space 3}log pseudolikelihood = {res:-575.30333}  (backed up)
Iteration 9:{space 3}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 16:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 17:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 19:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 27:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 29:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 30:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 32:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 35:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-575.30332}  (not concave)
Iteration 37:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-575.30332}  (backed up)
Iteration 39:{space 2}log pseudolikelihood = {res:-575.30329}  (backed up)
Iteration 40:{space 2}log pseudolikelihood = {res: -575.3032}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res: -575.3028}  (backed up)
Iteration 42:{space 2}log pseudolikelihood = {res: -575.3026}  (not concave)
Iteration 43:{space 2}log pseudolikelihood = {res:-575.30253}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res: -575.3024}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res: -575.3023}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-575.30166}  
Iteration 47:{space 2}log pseudolikelihood = {res:-575.30128}  (backed up)
Iteration 48:{space 2}log pseudolikelihood = {res:-575.30052}  (backed up)
Iteration 49:{space 2}log pseudolikelihood = {res: -575.2947}  (not concave)
Iteration 50:{space 2}log pseudolikelihood = {res:-575.29412}  (not concave)
Iteration 51:{space 2}log pseudolikelihood = {res:-575.29228}  
Iteration 52:{space 2}log pseudolikelihood = {res:-575.28294}  
Iteration 53:{space 2}log pseudolikelihood = {res:-575.25228}  
Iteration 54:{space 2}log pseudolikelihood = {res:-575.25228}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,009
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        20

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      50.5
{col 63}{txt}max{col 67}={res}{col 69}       177

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   346.36
{txt}Log pseudolikelihood = {res}-575.25228{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:20} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .765312{col 39}{space 2} .0945643{col 50}{space 1}   -2.16{col 59}{space 3}0.030{col 67}{space 4} .6007054{col 80}{space 3} .9750246
{txt}{space 22}age {c |}{col 27}{res}{space 2} 1.000884{col 39}{space 2} .0063756{col 50}{space 1}    0.14{col 59}{space 3}0.890{col 67}{space 4} .9884653{col 80}{space 3} 1.013458
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.063352{col 39}{space 2} .3289798{col 50}{space 1}    0.20{col 59}{space 3}0.843{col 67}{space 4} .5798725{col 80}{space 3} 1.949943
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.017006{col 39}{space 2} .3852254{col 50}{space 1}    0.04{col 59}{space 3}0.964{col 67}{space 4} .4840632{col 80}{space 3} 2.136706
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.121011{col 39}{space 2} .1781098{col 50}{space 1}    0.72{col 59}{space 3}0.472{col 67}{space 4} .8210478{col 80}{space 3} 1.530564
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2}  .887495{col 39}{space 2} .1256954{col 50}{space 1}   -0.84{col 59}{space 3}0.399{col 67}{space 4} .6723738{col 80}{space 3} 1.171443
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9415057{col 39}{space 2} .1826161{col 50}{space 1}   -0.31{col 59}{space 3}0.756{col 67}{space 4} .6437577{col 80}{space 3} 1.376967
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2}  1.25387{col 39}{space 2} .0756271{col 50}{space 1}    3.75{col 59}{space 3}0.000{col 67}{space 4}  1.11407{col 80}{space 3} 1.411214
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5954534{col 39}{space 2} .0957809{col 50}{space 1}   -3.22{col 59}{space 3}0.001{col 67}{space 4} .4344391{col 80}{space 3} .8161438
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .9914317{col 39}{space 2} .2468388{col 50}{space 1}   -0.03{col 59}{space 3}0.972{col 67}{space 4} .6086076{col 80}{space 3} 1.615058
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.012015{col 39}{space 2} .0147665{col 50}{space 1}    0.82{col 59}{space 3}0.413{col 67}{space 4} .9834831{col 80}{space 3} 1.041375
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9387194{col 39}{space 2}  .031368{col 50}{space 1}   -1.89{col 59}{space 3}0.058{col 67}{space 4} .8792092{col 80}{space 3} 1.002258
{txt}{space 17}gparties {c |}{col 27}{res}{space 2}  .951406{col 39}{space 2} .0754611{col 50}{space 1}   -0.63{col 59}{space 3}0.530{col 67}{space 4} .8144277{col 80}{space 3} 1.111423
{txt}{space 21}llsq {c |}{col 27}{res}{space 2} 1.051571{col 39}{space 2}  .048196{col 50}{space 1}    1.10{col 59}{space 3}0.273{col 67}{space 4} .9612269{col 80}{space 3} 1.150406
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2}  1.05871{col 39}{space 2} .0821287{col 50}{space 1}    0.74{col 59}{space 3}0.462{col 67}{space 4} .9093802{col 80}{space 3} 1.232561
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .4691789{col 39}{space 2} .2375844{col 50}{space 1}   -1.49{col 59}{space 3}0.135{col 67}{space 4} .1739019{col 80}{space 3} 1.265822
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 3.43e-33{col 39}{space 2} 6.44e-33{col 67}{space 4} 8.73e-35{col 80}{space 3} 1.35e-31
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}20
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea12.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A12. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea12.rtf"'})

{com}. 
. *************
. **Table A13**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_rr if p_niche==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -7329.622}  
Iteration 1:{space 3}log likelihood = {res:-6958.9507}  
Iteration 2:{space 3}log likelihood = {res:-6957.2688}  
Iteration 3:{space 3}log likelihood = {res:-6957.2667}  
Iteration 4:{space 3}log likelihood = {res:-6957.2667}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6799.4131}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6799.4131}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6795.2355}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6784.5943}  
Iteration 3:{space 3}log pseudolikelihood = {res: -6773.307}  
Iteration 4:{space 3}log pseudolikelihood = {res:-6773.2684}  
Iteration 5:{space 3}log pseudolikelihood = {res:-6773.2683}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,872
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}     663.4
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   429.26
{txt}Log pseudolikelihood = {res}-6773.2683{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.085291{col 39}{space 2}  .069422{col 50}{space 1}    1.28{col 59}{space 3}0.201{col 67}{space 4} .9574105{col 80}{space 3} 1.230253
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9845873{col 39}{space 2} .0025312{col 50}{space 1}   -6.04{col 59}{space 3}0.000{col 67}{space 4} .9796387{col 80}{space 3} .9895609
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.272932{col 39}{space 2}  .121779{col 50}{space 1}    2.52{col 59}{space 3}0.012{col 67}{space 4} 1.055291{col 80}{space 3} 1.535458
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.377359{col 39}{space 2} .1757225{col 50}{space 1}    2.51{col 59}{space 3}0.012{col 67}{space 4} 1.072634{col 80}{space 3} 1.768654
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8142103{col 39}{space 2} .0557206{col 50}{space 1}   -3.00{col 59}{space 3}0.003{col 67}{space 4} .7120073{col 80}{space 3} .9310836
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6457333{col 39}{space 2} .0596912{col 50}{space 1}   -4.73{col 59}{space 3}0.000{col 67}{space 4} .5387269{col 80}{space 3} .7739943
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.099285{col 39}{space 2} .1767506{col 50}{space 1}    8.81{col 59}{space 3}0.000{col 67}{space 4} 1.779934{col 80}{space 3} 2.475932
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9737085{col 39}{space 2}  .034105{col 50}{space 1}   -0.76{col 59}{space 3}0.447{col 67}{space 4} .9091067{col 80}{space 3} 1.042901
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3945227{col 39}{space 2} .0405806{col 50}{space 1}   -9.04{col 59}{space 3}0.000{col 67}{space 4} .3224909{col 80}{space 3} .4826436
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.217816{col 39}{space 2} .2410628{col 50}{space 1}    1.00{col 59}{space 3}0.319{col 67}{space 4} .8262075{col 80}{space 3} 1.795041
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.017003{col 39}{space 2} .0115736{col 50}{space 1}    1.48{col 59}{space 3}0.138{col 67}{space 4}   .99457{col 80}{space 3} 1.039942
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} 1.028076{col 39}{space 2} .0099719{col 50}{space 1}    2.85{col 59}{space 3}0.004{col 67}{space 4} 1.008716{col 80}{space 3} 1.047807
{txt}{space 13}lpss_mod3_rr {c |}{col 27}{res}{space 2} 1.088265{col 39}{space 2} .0517042{col 50}{space 1}    1.78{col 59}{space 3}0.075{col 67}{space 4} .9915016{col 80}{space 3} 1.194471
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0767711{col 39}{space 2} .0302302{col 50}{space 1}   -6.52{col 59}{space 3}0.000{col 67}{space 4} .0354829{col 80}{space 3} .1661024
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4341575{col 39}{space 2} .1615785{col 67}{space 4} .2093435{col 80}{space 3} .9003995
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_rr if p_niche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2260.6707}  
Iteration 1:{space 3}log likelihood = {res:-2254.9811}  
Iteration 2:{space 3}log likelihood = {res:-2254.9761}  
Iteration 3:{space 3}log likelihood = {res:-2254.9761}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2243.7395}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2243.7395}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2237.1829}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2235.6643}  
Iteration 3:{space 3}log pseudolikelihood = {res: -2235.114}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2235.1136}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2235.1136}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,515
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     141.1
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   226.95
{txt}Log pseudolikelihood = {res}-2235.1136{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:32} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8241247{col 39}{space 2} .0883332{col 50}{space 1}   -1.80{col 59}{space 3}0.071{col 67}{space 4} .6679709{col 80}{space 3} 1.016783
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9932116{col 39}{space 2} .0028186{col 50}{space 1}   -2.40{col 59}{space 3}0.016{col 67}{space 4} .9877026{col 80}{space 3} .9987513
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.295912{col 39}{space 2} .1589021{col 50}{space 1}    2.11{col 59}{space 3}0.035{col 67}{space 4} 1.019068{col 80}{space 3} 1.647966
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.034999{col 39}{space 2} .1455805{col 50}{space 1}    0.24{col 59}{space 3}0.807{col 67}{space 4} .7856192{col 80}{space 3} 1.363541
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.125684{col 39}{space 2} .1156292{col 50}{space 1}    1.15{col 59}{space 3}0.249{col 67}{space 4} .9204108{col 80}{space 3} 1.376737
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.614283{col 39}{space 2} .1888888{col 50}{space 1}    4.09{col 59}{space 3}0.000{col 67}{space 4} 1.283452{col 80}{space 3}  2.03039
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .7965918{col 39}{space 2} .0664362{col 50}{space 1}   -2.73{col 59}{space 3}0.006{col 67}{space 4} .6764647{col 80}{space 3} .9380511
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.117723{col 39}{space 2} .0416535{col 50}{space 1}    2.99{col 59}{space 3}0.003{col 67}{space 4} 1.038994{col 80}{space 3} 1.202417
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4276405{col 39}{space 2} .0406979{col 50}{space 1}   -8.93{col 59}{space 3}0.000{col 67}{space 4} .3548716{col 80}{space 3} .5153311
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7548847{col 39}{space 2} .1456935{col 50}{space 1}   -1.46{col 59}{space 3}0.145{col 67}{space 4} .5171278{col 80}{space 3} 1.101954
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9905621{col 39}{space 2} .0117684{col 50}{space 1}   -0.80{col 59}{space 3}0.425{col 67}{space 4}  .967763{col 80}{space 3} 1.013898
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9943279{col 39}{space 2}  .007674{col 50}{space 1}   -0.74{col 59}{space 3}0.461{col 67}{space 4} .9794002{col 80}{space 3} 1.009483
{txt}{space 13}lpss_mod3_rr {c |}{col 27}{res}{space 2} .9265494{col 39}{space 2} .0312337{col 50}{space 1}   -2.26{col 59}{space 3}0.024{col 67}{space 4}  .867311{col 80}{space 3} .9898339
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .5978375{col 39}{space 2} .2454765{col 50}{space 1}   -1.25{col 59}{space 3}0.210{col 67}{space 4} .2673459{col 80}{space 3} 1.336881
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1827721{col 39}{space 2} .1365153{col 67}{space 4} .0422799{col 80}{space 3} .7901061
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}32
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_rr if p_radicalrl_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5446.3476}  
Iteration 1:{space 3}log likelihood = {res:-4783.3115}  
Iteration 2:{space 3}log likelihood = {res:-4768.0918}  
Iteration 3:{space 3}log likelihood = {res:-4767.9961}  
Iteration 4:{space 3}log likelihood = {res:-4767.9961}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4455.0036}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4455.0036}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4420.8522}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4417.8791}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4417.5301}  
Iteration 4:{space 3}log pseudolikelihood = {res:-4417.5202}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4417.5202}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,042
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     642.1
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   824.90
{txt}Log pseudolikelihood = {res}-4417.5202{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.314889{col 40}{space 2} .0917597{col 51}{space 1}    3.92{col 60}{space 3}0.000{col 68}{space 4} 1.146801{col 81}{space 3} 1.507615
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9876273{col 40}{space 2}  .003009{col 51}{space 1}   -4.09{col 60}{space 3}0.000{col 68}{space 4} .9817473{col 81}{space 3} .9935426
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.374579{col 40}{space 2} .1878102{col 51}{space 1}    2.33{col 60}{space 3}0.020{col 68}{space 4} 1.051645{col 81}{space 3} 1.796678
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.161073{col 40}{space 2} .1874257{col 51}{space 1}    0.93{col 60}{space 3}0.355{col 68}{space 4} .8461645{col 81}{space 3} 1.593178
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.704505{col 40}{space 2}  .261811{col 51}{space 1}   10.28{col 60}{space 3}0.000{col 68}{space 4} 2.237107{col 81}{space 3} 3.269556
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7410032{col 40}{space 2} .0603894{col 51}{space 1}   -3.68{col 60}{space 3}0.000{col 68}{space 4} .6316113{col 81}{space 3} .8693411
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5431809{col 40}{space 2} .0520924{col 51}{space 1}   -6.36{col 60}{space 3}0.000{col 68}{space 4} .4501033{col 81}{space 3} .6555062
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.037614{col 40}{space 2} .0420346{col 51}{space 1}    0.91{col 60}{space 3}0.362{col 68}{space 4} .9584131{col 81}{space 3} 1.123359
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3517085{col 40}{space 2}  .045749{col 51}{space 1}   -8.03{col 60}{space 3}0.000{col 68}{space 4} .2725596{col 81}{space 3} .4538416
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.159598{col 40}{space 2} .2662033{col 51}{space 1}    0.65{col 60}{space 3}0.519{col 68}{space 4} .7394374{col 81}{space 3} 1.818502
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9685953{col 40}{space 2} .0263124{col 51}{space 1}   -1.17{col 60}{space 3}0.240{col 68}{space 4} .9183729{col 81}{space 3} 1.021564
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2}  1.09017{col 40}{space 2} .0349436{col 51}{space 1}    2.69{col 60}{space 3}0.007{col 68}{space 4} 1.023788{col 81}{space 3} 1.160855
{txt}{space 14}lpss_mod3_rr {c |}{col 28}{res}{space 2}  1.66403{col 40}{space 2} .2401163{col 51}{space 1}    3.53{col 60}{space 3}0.000{col 68}{space 4} 1.254106{col 81}{space 3} 2.207943
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}  .031794{col 40}{space 2} .0210871{col 51}{space 1}   -5.20{col 60}{space 3}0.000{col 68}{space 4} .0086655{col 81}{space 3} .1166537
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 1.961046{col 40}{space 2} .8772571{col 68}{space 4} .8160373{col 81}{space 3} 4.712653
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_rr if p_radicalrl_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -1323.918}  
Iteration 1:{space 3}log likelihood = {res:-1318.7952}  
Iteration 2:{space 3}log likelihood = {res:-1318.7809}  
Iteration 3:{space 3}log likelihood = {res:-1318.7809}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1292.3811}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1292.3811}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1290.1468}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1286.456}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1286.3781}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1286.3776}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1286.3776}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,716
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     100.6
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   433.97
{txt}Log pseudolikelihood = {res}-1286.3776{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8875176{col 40}{space 2} .1334048{col 51}{space 1}   -0.79{col 60}{space 3}0.427{col 68}{space 4} .6610449{col 81}{space 3} 1.191579
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9927242{col 40}{space 2} .0037911{col 51}{space 1}   -1.91{col 60}{space 3}0.056{col 68}{space 4} .9853214{col 81}{space 3} 1.000183
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.156397{col 40}{space 2} .1983367{col 51}{space 1}    0.85{col 60}{space 3}0.397{col 68}{space 4} .8262572{col 81}{space 3} 1.618448
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7481481{col 40}{space 2} .1538458{col 51}{space 1}   -1.41{col 60}{space 3}0.158{col 68}{space 4} .4999774{col 81}{space 3} 1.119502
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .6647014{col 40}{space 2} .0519968{col 51}{space 1}   -5.22{col 60}{space 3}0.000{col 68}{space 4} .5702177{col 81}{space 3} .7748408
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.102007{col 40}{space 2} .1842367{col 51}{space 1}    0.58{col 60}{space 3}0.561{col 68}{space 4} .7941051{col 81}{space 3} 1.529293
{txt}{space 14}High income  {c |}{col 28}{res}{space 2}  1.76388{col 40}{space 2} .2803651{col 51}{space 1}    3.57{col 60}{space 3}0.000{col 68}{space 4} 1.291732{col 81}{space 3} 2.408606
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.057803{col 40}{space 2} .0528777{col 51}{space 1}    1.12{col 60}{space 3}0.261{col 68}{space 4} .9590796{col 81}{space 3} 1.166688
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2}  .390851{col 40}{space 2} .0424486{col 51}{space 1}   -8.65{col 60}{space 3}0.000{col 68}{space 4} .3159119{col 81}{space 3} .4835667
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.314489{col 40}{space 2} .4247664{col 51}{space 1}    0.85{col 60}{space 3}0.397{col 68}{space 4} .6977472{col 81}{space 3} 2.476371
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2}   .97522{col 40}{space 2}  .020617{col 51}{space 1}   -1.19{col 60}{space 3}0.235{col 68}{space 4} .9356372{col 81}{space 3} 1.016477
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9846025{col 40}{space 2} .0165476{col 51}{space 1}   -0.92{col 60}{space 3}0.356{col 68}{space 4} .9526981{col 81}{space 3} 1.017575
{txt}{space 14}lpss_mod3_rr {c |}{col 28}{res}{space 2} .9332872{col 40}{space 2} .1107168{col 51}{space 1}   -0.58{col 60}{space 3}0.561{col 68}{space 4} .7396673{col 81}{space 3}  1.17759
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .9674362{col 40}{space 2} .5026162{col 51}{space 1}   -0.06{col 60}{space 3}0.949{col 68}{space 4} .3494572{col 81}{space 3} 2.678247
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .4505682{col 40}{space 2} .1684639{col 68}{space 4} .2165225{col 81}{space 3} .9376009
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}27
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_rr if p_green_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3539.2592}  
Iteration 1:{space 3}log likelihood = {res:-2399.6298}  
Iteration 2:{space 3}log likelihood = {res:-2321.8752}  
Iteration 3:{space 3}log likelihood = {res:-2315.0344}  
Iteration 4:{space 3}log likelihood = {res:-2314.9809}  
Iteration 5:{space 3}log likelihood = {res:-2314.9809}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2255.3438}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2255.3438}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2248.6385}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2245.0675}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2244.6148}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2244.5976}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2244.5975}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,275
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        70
{col 63}{txt}avg{col 67}={res}{col 69}     622.4
{col 63}{txt}max{col 67}={res}{col 69}     1,256

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   405.41
{txt}Log pseudolikelihood = {res}-2244.5975{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .679792{col 39}{space 2} .0504793{col 50}{space 1}   -5.20{col 59}{space 3}0.000{col 67}{space 4} .5877172{col 80}{space 3} .7862918
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9787201{col 39}{space 2} .0037024{col 50}{space 1}   -5.69{col 59}{space 3}0.000{col 67}{space 4} .9714903{col 80}{space 3} .9860038
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.741768{col 39}{space 2} .4731365{col 50}{space 1}    2.04{col 59}{space 3}0.041{col 67}{space 4} 1.022748{col 80}{space 3} 2.966279
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 3.316475{col 39}{space 2} 1.147505{col 50}{space 1}    3.47{col 59}{space 3}0.001{col 67}{space 4} 1.683294{col 80}{space 3} 6.534218
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.154265{col 39}{space 2} .1572902{col 50}{space 1}    1.05{col 59}{space 3}0.292{col 67}{space 4} .8837177{col 80}{space 3} 1.507641
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.041247{col 39}{space 2} .1387244{col 50}{space 1}    0.30{col 59}{space 3}0.762{col 67}{space 4} .8019533{col 80}{space 3} 1.351944
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9499399{col 39}{space 2} .1896253{col 50}{space 1}   -0.26{col 59}{space 3}0.797{col 67}{space 4} .6423634{col 80}{space 3}  1.40479
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9125574{col 39}{space 2}  .042348{col 50}{space 1}   -1.97{col 59}{space 3}0.049{col 67}{space 4} .8332197{col 80}{space 3} .9994496
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5370122{col 39}{space 2} .0561041{col 50}{space 1}   -5.95{col 59}{space 3}0.000{col 67}{space 4} .4375778{col 80}{space 3}  .659042
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.504499{col 39}{space 2} .4252277{col 50}{space 1}    1.45{col 59}{space 3}0.148{col 67}{space 4} .8645906{col 80}{space 3} 2.618022
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9897938{col 39}{space 2} .0370451{col 50}{space 1}   -0.27{col 59}{space 3}0.784{col 67}{space 4} .9197859{col 80}{space 3}  1.06513
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.712251{col 39}{space 2} .1639214{col 50}{space 1}    5.62{col 59}{space 3}0.000{col 67}{space 4} 1.419313{col 80}{space 3}  2.06565
{txt}{space 13}lpss_mod3_rr {c |}{col 27}{res}{space 2} .9128913{col 39}{space 2} .1399207{col 50}{space 1}   -0.59{col 59}{space 3}0.552{col 67}{space 4}  .676011{col 80}{space 3} 1.232777
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0013951{col 39}{space 2} .0013544{col 50}{space 1}   -6.77{col 59}{space 3}0.000{col 67}{space 4} .0002081{col 80}{space 3} .0093536
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.804702{col 39}{space 2} .7725403{col 67}{space 4} .7798854{col 80}{space 3} 4.176188
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_rr if p_green_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-596.43627}  
Iteration 1:{space 3}log likelihood = {res:-595.73384}  
Iteration 2:{space 3}log likelihood = {res:-595.73363}  
Iteration 3:{space 3}log likelihood = {res:-595.73363}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-605.92063}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-605.92063}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-595.99555}  
Iteration 2:{space 3}log pseudolikelihood = {res:-595.97303}  
Iteration 3:{space 3}log pseudolikelihood = {res:-595.96258}  (backed up)
Iteration 4:{space 3}log pseudolikelihood = {res:-595.95754}  (backed up)
Iteration 5:{space 3}log pseudolikelihood = {res:-595.95506}  (backed up)
Iteration 6:{space 3}log pseudolikelihood = {res:-595.95383}  (backed up)
Iteration 7:{space 3}log pseudolikelihood = {res:-595.95322}  (backed up)
Iteration 8:{space 3}log pseudolikelihood = {res:-595.95315}  (backed up)
Iteration 9:{space 3}log pseudolikelihood = {res:-595.95314}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 16:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 17:{space 2}log pseudolikelihood = {res:-595.95313}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 19:{space 2}log pseudolikelihood = {res:-595.95313}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-595.95313}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 22:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-595.95313}  (not concave)
Iteration 25:{space 2}log pseudolikelihood = {res:-595.95313}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res:-595.95313}  (not concave)
Iteration 27:{space 2}log pseudolikelihood = {res:-595.95313}  (not concave)
Iteration 28:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-595.95313}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res:-595.95313}  
Iteration 32:{space 2}log pseudolikelihood = {res:-595.95313}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-595.95312}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-595.95312}  (not concave)
Iteration 35:{space 2}log pseudolikelihood = {res:-595.95312}  
Iteration 36:{space 2}log pseudolikelihood = {res:-595.95311}  (not concave)
Iteration 37:{space 2}log pseudolikelihood = {res: -595.9531}  
Iteration 38:{space 2}log pseudolikelihood = {res:  -595.953}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res:-595.95296}  
Iteration 40:{space 2}log pseudolikelihood = {res:-595.95274}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res:-595.95232}  (backed up)
Iteration 42:{space 2}log pseudolikelihood = {res:-595.95147}  (backed up)
Iteration 43:{space 2}log pseudolikelihood = {res:-595.95063}  (backed up)
Iteration 44:{space 2}log pseudolikelihood = {res:-595.95021}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res:-595.94988}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-595.94881}  (not concave)
Iteration 47:{space 2}log pseudolikelihood = {res: -595.9487}  
Iteration 48:{space 2}log pseudolikelihood = {res:-595.92291}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-595.92034}  (not concave)
Iteration 50:{space 2}log pseudolikelihood = {res:-595.91625}  (not concave)
Iteration 51:{space 2}log pseudolikelihood = {res:-595.91462}  (not concave)
Iteration 52:{space 2}log pseudolikelihood = {res:-595.90945}  (not concave)
Iteration 53:{space 2}log pseudolikelihood = {res:-595.90534}  
Iteration 54:{space 2}log pseudolikelihood = {res:-595.73367}  
Iteration 55:{space 2}log pseudolikelihood = {res:-595.73363}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,045
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        21

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      49.8
{col 63}{txt}max{col 67}={res}{col 69}       177

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   134.26
{txt}Log pseudolikelihood = {res}-595.73363{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:21} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .7925663{col 39}{space 2} .1026762{col 50}{space 1}   -1.79{col 59}{space 3}0.073{col 67}{space 4} .6148417{col 80}{space 3} 1.021664
{txt}{space 22}age {c |}{col 27}{res}{space 2} 1.000597{col 39}{space 2} .0062913{col 50}{space 1}    0.09{col 59}{space 3}0.924{col 67}{space 4} .9883424{col 80}{space 3} 1.013004
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .9609691{col 39}{space 2} .2808155{col 50}{space 1}   -0.14{col 59}{space 3}0.892{col 67}{space 4} .5419638{col 80}{space 3} 1.703917
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8472474{col 39}{space 2} .2924122{col 50}{space 1}   -0.48{col 59}{space 3}0.631{col 67}{space 4} .4307579{col 80}{space 3}  1.66643
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.209136{col 39}{space 2} .1993212{col 50}{space 1}    1.15{col 59}{space 3}0.249{col 67}{space 4}  .875302{col 80}{space 3} 1.670292
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9601201{col 39}{space 2} .1258995{col 50}{space 1}   -0.31{col 59}{space 3}0.756{col 67}{space 4} .7425204{col 80}{space 3} 1.241488
{txt}{space 13}High income  {c |}{col 27}{res}{space 2}  1.01459{col 39}{space 2} .1863724{col 50}{space 1}    0.08{col 59}{space 3}0.937{col 67}{space 4} .7078338{col 80}{space 3} 1.454286
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.260536{col 39}{space 2} .0782573{col 50}{space 1}    3.73{col 59}{space 3}0.000{col 67}{space 4} 1.116119{col 80}{space 3} 1.423639
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2}  .557496{col 39}{space 2} .0985011{col 50}{space 1}   -3.31{col 59}{space 3}0.001{col 67}{space 4} .3943187{col 80}{space 3} .7881994
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .9104296{col 39}{space 2} .1749631{col 50}{space 1}   -0.49{col 59}{space 3}0.625{col 67}{space 4} .6246913{col 80}{space 3} 1.326867
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.005655{col 39}{space 2} .0109927{col 50}{space 1}    0.52{col 59}{space 3}0.606{col 67}{space 4} .9843387{col 80}{space 3} 1.027432
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9347261{col 39}{space 2} .0326938{col 50}{space 1}   -1.93{col 59}{space 3}0.054{col 67}{space 4} .8727945{col 80}{space 3} 1.001052
{txt}{space 13}lpss_mod3_rr {c |}{col 27}{res}{space 2} 1.009354{col 39}{space 2} .0333156{col 50}{space 1}    0.28{col 59}{space 3}0.778{col 67}{space 4} .9461242{col 80}{space 3}  1.07681
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .6166026{col 39}{space 2} .2585076{col 50}{space 1}   -1.15{col 59}{space 3}0.249{col 67}{space 4} .2711086{col 80}{space 3} 1.402385
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 3.06e-32{col 39}{space 2} 1.02e-30{col 67}{space 4} 1.26e-60{col 80}{space 3} .0007446
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}21
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea13.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A13. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea13.rtf"'})

{com}. 
. *************
. **Table A14**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_higher if p_niche==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-7330.8872}  
Iteration 1:{space 3}log likelihood = {res:-6961.9813}  
Iteration 2:{space 3}log likelihood = {res:-6960.4046}  
Iteration 3:{space 3}log likelihood = {res:-6960.4027}  
Iteration 4:{space 3}log likelihood = {res:-6960.4027}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6798.0551}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6798.0551}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6793.9871}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6778.3834}  
Iteration 3:{space 3}log pseudolikelihood = {res: -6773.655}  
Iteration 4:{space 3}log pseudolikelihood = {res:-6773.5829}  
Iteration 5:{space 3}log pseudolikelihood = {res:-6773.5827}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,872
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}     663.4
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   420.62
{txt}Log pseudolikelihood = {res}-6773.5827{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.085456{col 39}{space 2} .0694314{col 50}{space 1}    1.28{col 59}{space 3}0.200{col 67}{space 4} .9575574{col 80}{space 3} 1.230437
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9845557{col 39}{space 2} .0025269{col 50}{space 1}   -6.06{col 59}{space 3}0.000{col 67}{space 4} .9796154{col 80}{space 3} .9895209
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.270401{col 39}{space 2} .1211814{col 50}{space 1}    2.51{col 59}{space 3}0.012{col 67}{space 4} 1.053771{col 80}{space 3} 1.531566
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.373199{col 39}{space 2} .1746043{col 50}{space 1}    2.49{col 59}{space 3}0.013{col 67}{space 4} 1.070291{col 80}{space 3} 1.761835
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8142208{col 39}{space 2}  .055732{col 50}{space 1}   -3.00{col 59}{space 3}0.003{col 67}{space 4} .7119981{col 80}{space 3} .9311196
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6460935{col 39}{space 2}  .059684{col 50}{space 1}   -4.73{col 59}{space 3}0.000{col 67}{space 4} .5390936{col 80}{space 3} .7743308
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.099516{col 39}{space 2} .1766643{col 50}{space 1}    8.81{col 59}{space 3}0.000{col 67}{space 4} 1.780306{col 80}{space 3} 2.475961
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9735807{col 39}{space 2} .0341085{col 50}{space 1}   -0.76{col 59}{space 3}0.445{col 67}{space 4} .9089728{col 80}{space 3} 1.042781
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3943723{col 39}{space 2} .0405506{col 50}{space 1}   -9.05{col 59}{space 3}0.000{col 67}{space 4} .3223912{col 80}{space 3} .4824248
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.218472{col 39}{space 2} .2412833{col 50}{space 1}    1.00{col 59}{space 3}0.318{col 67}{space 4} .8265322{col 80}{space 3}  1.79627
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.014149{col 39}{space 2} .0108157{col 50}{space 1}    1.32{col 59}{space 3}0.188{col 67}{space 4} .9931711{col 80}{space 3} 1.035571
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} 1.028958{col 39}{space 2} .0105217{col 50}{space 1}    2.79{col 59}{space 3}0.005{col 67}{space 4} 1.008541{col 80}{space 3} 1.049789
{txt}{space 14}lpss_higher {c |}{col 27}{res}{space 2} 1.096786{col 39}{space 2} .0969255{col 50}{space 1}    1.05{col 59}{space 3}0.296{col 67}{space 4} .9223572{col 80}{space 3} 1.304201
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0771212{col 39}{space 2} .0305451{col 50}{space 1}   -6.47{col 59}{space 3}0.000{col 67}{space 4} .0354847{col 80}{space 3} .1676123
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2}   .44139{col 39}{space 2} .1659387{col 67}{space 4} .2112599{col 80}{space 3} .9222063
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_higher if p_niche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2262.4843}  
Iteration 1:{space 3}log likelihood = {res:-2256.9679}  
Iteration 2:{space 3}log likelihood = {res:-2256.9634}  
Iteration 3:{space 3}log likelihood = {res:-2256.9634}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -2244.133}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -2244.133}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2237.7391}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2236.3981}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2235.8364}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2235.8058}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2235.8057}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,515
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     141.1
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   233.77
{txt}Log pseudolikelihood = {res}-2235.8057{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:32} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8229685{col 39}{space 2}  .088317{col 50}{space 1}   -1.82{col 59}{space 3}0.069{col 67}{space 4} .6668625{col 80}{space 3} 1.015617
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9932385{col 39}{space 2}  .002817{col 50}{space 1}   -2.39{col 59}{space 3}0.017{col 67}{space 4} .9877325{col 80}{space 3} .9987752
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.299112{col 39}{space 2} .1600683{col 50}{space 1}    2.12{col 59}{space 3}0.034{col 67}{space 4} 1.020392{col 80}{space 3} 1.653964
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.044408{col 39}{space 2} .1471843{col 50}{space 1}    0.31{col 59}{space 3}0.758{col 67}{space 4} .7923435{col 80}{space 3}  1.37666
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.125226{col 39}{space 2} .1155646{col 50}{space 1}    1.15{col 59}{space 3}0.251{col 67}{space 4} .9200652{col 80}{space 3} 1.376135
{txt}{space 13}High income  {c |}{col 27}{res}{space 2}   1.6103{col 39}{space 2} .1878775{col 50}{space 1}    4.08{col 59}{space 3}0.000{col 67}{space 4} 1.281135{col 80}{space 3} 2.024037
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2}  .794386{col 39}{space 2} .0664246{col 50}{space 1}   -2.75{col 59}{space 3}0.006{col 67}{space 4} .6743047{col 80}{space 3} .9358515
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.118779{col 39}{space 2} .0417891{col 50}{space 1}    3.00{col 59}{space 3}0.003{col 67}{space 4}   1.0398{col 80}{space 3} 1.203757
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4285393{col 39}{space 2}  .040816{col 50}{space 1}   -8.90{col 59}{space 3}0.000{col 67}{space 4} .3555646{col 80}{space 3} .5164912
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7536502{col 39}{space 2} .1470744{col 50}{space 1}   -1.45{col 59}{space 3}0.147{col 67}{space 4} .5141128{col 80}{space 3} 1.104794
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2}  .993371{col 39}{space 2} .0119689{col 50}{space 1}   -0.55{col 59}{space 3}0.581{col 67}{space 4} .9701873{col 80}{space 3} 1.017109
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9924283{col 39}{space 2}  .007612{col 50}{space 1}   -0.99{col 59}{space 3}0.322{col 67}{space 4} .9776206{col 80}{space 3}  1.00746
{txt}{space 14}lpss_higher {c |}{col 27}{res}{space 2} .9489199{col 39}{space 2}  .048377{col 50}{space 1}   -1.03{col 59}{space 3}0.304{col 67}{space 4} .8586859{col 80}{space 3} 1.048636
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .6043138{col 39}{space 2} .2552737{col 50}{space 1}   -1.19{col 59}{space 3}0.233{col 67}{space 4} .2640576{col 80}{space 3} 1.383013
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .2018382{col 39}{space 2} .1448235{col 67}{space 4} .0494591{col 80}{space 3} .8236829
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}32
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_higher if p_radicalrl_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -5445.093}  
Iteration 1:{space 3}log likelihood = {res:-4784.2211}  
Iteration 2:{space 3}log likelihood = {res:-4773.1756}  
Iteration 3:{space 3}log likelihood = {res:-4773.1184}  
Iteration 4:{space 3}log likelihood = {res:-4773.1184}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4454.0368}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4454.0368}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4429.5767}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4419.8152}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4418.6506}  
Iteration 4:{space 3}log pseudolikelihood = {res:-4418.5745}  
Iteration 5:{space 3}log pseudolikelihood = {res: -4418.574}  
Iteration 6:{space 3}log pseudolikelihood = {res: -4418.574}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,042
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     642.1
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   822.14
{txt}Log pseudolikelihood = {res}-4418.574{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.315005{col 40}{space 2} .0917949{col 51}{space 1}    3.92{col 60}{space 3}0.000{col 68}{space 4} 1.146855{col 81}{space 3} 1.507808
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9875872{col 40}{space 2} .0030079{col 51}{space 1}   -4.10{col 60}{space 3}0.000{col 68}{space 4} .9817094{col 81}{space 3} .9935002
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.370598{col 40}{space 2} .1869464{col 51}{space 1}    2.31{col 60}{space 3}0.021{col 68}{space 4} 1.049079{col 81}{space 3} 1.790655
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.156358{col 40}{space 2} .1862028{col 51}{space 1}    0.90{col 60}{space 3}0.367{col 68}{space 4} .8433879{col 81}{space 3} 1.585467
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.705615{col 40}{space 2} .2618291{col 51}{space 1}   10.29{col 60}{space 3}0.000{col 68}{space 4}  2.23817{col 81}{space 3} 3.270687
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7410878{col 40}{space 2} .0604226{col 51}{space 1}   -3.68{col 60}{space 3}0.000{col 68}{space 4} .6316395{col 81}{space 3} .8695009
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5437352{col 40}{space 2} .0520824{col 51}{space 1}   -6.36{col 60}{space 3}0.000{col 68}{space 4} .4506651{col 81}{space 3} .6560257
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.037477{col 40}{space 2} .0420707{col 51}{space 1}    0.91{col 60}{space 3}0.364{col 68}{space 4} .9582115{col 81}{space 3} 1.123299
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3514065{col 40}{space 2} .0456763{col 51}{space 1}   -8.05{col 60}{space 3}0.000{col 68}{space 4} .2723763{col 81}{space 3} .4533673
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.160288{col 40}{space 2} .2663643{col 51}{space 1}    0.65{col 60}{space 3}0.517{col 68}{space 4} .7398738{col 81}{space 3} 1.819591
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9564785{col 40}{space 2} .0258415{col 51}{space 1}   -1.65{col 60}{space 3}0.100{col 68}{space 4} .9071478{col 81}{space 3} 1.008492
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 1.098604{col 40}{space 2} .0350372{col 51}{space 1}    2.95{col 60}{space 3}0.003{col 68}{space 4} 1.032034{col 81}{space 3} 1.169467
{txt}{space 15}lpss_higher {c |}{col 28}{res}{space 2} 1.751564{col 40}{space 2} .3437071{col 51}{space 1}    2.86{col 60}{space 3}0.004{col 68}{space 4} 1.192328{col 81}{space 3} 2.573097
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0313297{col 40}{space 2} .0210015{col 51}{space 1}   -5.17{col 60}{space 3}0.000{col 68}{space 4}  .008421{col 81}{space 3} .1165598
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 2.049124{col 40}{space 2} .8822091{col 68}{space 4} .8812532{col 81}{space 3} 4.764704
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_higher if p_radicalrl_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1323.8939}  
Iteration 1:{space 3}log likelihood = {res:-1318.8368}  
Iteration 2:{space 3}log likelihood = {res:-1318.8232}  
Iteration 3:{space 3}log likelihood = {res:-1318.8231}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1292.4642}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1292.4642}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1290.0559}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1286.5213}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1286.4707}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1286.4706}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1286.4706}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,716
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     100.6
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   423.46
{txt}Log pseudolikelihood = {res}-1286.4706{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8875635{col 40}{space 2} .1333814{col 51}{space 1}   -0.79{col 60}{space 3}0.427{col 68}{space 4} .6611232{col 81}{space 3} 1.191561
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9927121{col 40}{space 2} .0037978{col 51}{space 1}   -1.91{col 60}{space 3}0.056{col 68}{space 4} .9852964{col 81}{space 3} 1.000184
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.154826{col 40}{space 2} .1979888{col 51}{space 1}    0.84{col 60}{space 3}0.401{col 68}{space 4} .8252442{col 81}{space 3} 1.616034
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7484161{col 40}{space 2} .1537572{col 51}{space 1}   -1.41{col 60}{space 3}0.158{col 68}{space 4} .5003447{col 81}{space 3} 1.119482
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .6634603{col 40}{space 2} .0523116{col 51}{space 1}   -5.20{col 60}{space 3}0.000{col 68}{space 4} .5684608{col 81}{space 3} .7743358
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.101492{col 40}{space 2} .1838481{col 51}{space 1}    0.58{col 60}{space 3}0.562{col 68}{space 4} .7941615{col 81}{space 3} 1.527756
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.760372{col 40}{space 2} .2797799{col 51}{space 1}    3.56{col 60}{space 3}0.000{col 68}{space 4} 1.289202{col 81}{space 3} 2.403742
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.058383{col 40}{space 2} .0526885{col 51}{space 1}    1.14{col 60}{space 3}0.254{col 68}{space 4} .9599937{col 81}{space 3} 1.166857
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3908846{col 40}{space 2} .0425284{col 51}{space 1}   -8.63{col 60}{space 3}0.000{col 68}{space 4} .3158185{col 81}{space 3} .4837931
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.322805{col 40}{space 2} .4255263{col 51}{space 1}    0.87{col 60}{space 3}0.384{col 68}{space 4} .7041692{col 81}{space 3} 2.484932
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2}   .97492{col 40}{space 2} .0203482{col 51}{space 1}   -1.22{col 60}{space 3}0.224{col 68}{space 4} .9358429{col 81}{space 3} 1.015629
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9835301{col 40}{space 2} .0165238{col 51}{space 1}   -0.99{col 60}{space 3}0.323{col 68}{space 4} .9516714{col 81}{space 3} 1.016455
{txt}{space 15}lpss_higher {c |}{col 28}{res}{space 2} .9848739{col 40}{space 2} .1171026{col 51}{space 1}   -0.13{col 60}{space 3}0.898{col 68}{space 4} .7801387{col 81}{space 3} 1.243339
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .9807664{col 40}{space 2} .5040534{col 51}{space 1}   -0.04{col 60}{space 3}0.970{col 68}{space 4} .3581793{col 81}{space 3} 2.685534
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .4551894{col 40}{space 2} .1685096{col 68}{space 4} .2203333{col 81}{space 3} .9403816
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}27
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_higher if p_green_vs_mainstream==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3539.2531}  
Iteration 1:{space 3}log likelihood = {res:-2399.6981}  
Iteration 2:{space 3}log likelihood = {res: -2322.046}  
Iteration 3:{space 3}log likelihood = {res:-2315.2187}  
Iteration 4:{space 3}log likelihood = {res:-2315.1662}  
Iteration 5:{space 3}log likelihood = {res:-2315.1662}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2255.5405}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2255.5405}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2248.9467}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2245.2703}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2244.8081}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2244.7908}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2244.7907}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,275
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        70
{col 63}{txt}avg{col 67}={res}{col 69}     622.4
{col 63}{txt}max{col 67}={res}{col 69}     1,256

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   412.87
{txt}Log pseudolikelihood = {res}-2244.7907{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .6796624{col 39}{space 2} .0504275{col 50}{space 1}   -5.20{col 59}{space 3}0.000{col 67}{space 4} .5876767{col 80}{space 3} .7860461
{txt}{space 22}age {c |}{col 27}{res}{space 2}  .978717{col 39}{space 2} .0036983{col 50}{space 1}   -5.69{col 59}{space 3}0.000{col 67}{space 4} .9714953{col 80}{space 3} .9859925
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.740626{col 39}{space 2} .4721237{col 50}{space 1}    2.04{col 59}{space 3}0.041{col 67}{space 4} 1.022886{col 80}{space 3} 2.961989
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  3.32249{col 39}{space 2}  1.15072{col 50}{space 1}    3.47{col 59}{space 3}0.001{col 67}{space 4} 1.685219{col 80}{space 3}  6.55045
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.154101{col 39}{space 2} .1571576{col 50}{space 1}    1.05{col 59}{space 3}0.293{col 67}{space 4} .8837574{col 80}{space 3} 1.507144
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.041093{col 39}{space 2}  .138677{col 50}{space 1}    0.30{col 59}{space 3}0.762{col 67}{space 4} .8018747{col 80}{space 3} 1.351675
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9488885{col 39}{space 2} .1895937{col 50}{space 1}   -0.26{col 59}{space 3}0.793{col 67}{space 4} .6414163{col 80}{space 3} 1.403752
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2}  .912561{col 39}{space 2}  .042254{col 50}{space 1}   -1.98{col 59}{space 3}0.048{col 67}{space 4} .8333914{col 80}{space 3} .9992515
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5367718{col 39}{space 2} .0559968{col 50}{space 1}   -5.96{col 59}{space 3}0.000{col 67}{space 4} .4375131{col 80}{space 3} .6585493
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.502786{col 39}{space 2} .4244287{col 50}{space 1}    1.44{col 59}{space 3}0.149{col 67}{space 4} .8639612{col 80}{space 3} 2.613968
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9960447{col 39}{space 2} .0326633{col 50}{space 1}   -0.12{col 59}{space 3}0.904{col 67}{space 4} .9340399{col 80}{space 3} 1.062166
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.728049{col 39}{space 2} .1704304{col 50}{space 1}    5.55{col 59}{space 3}0.000{col 67}{space 4} 1.424313{col 80}{space 3} 2.096556
{txt}{space 14}lpss_higher {c |}{col 27}{res}{space 2}  1.00375{col 39}{space 2}  .255723{col 50}{space 1}    0.01{col 59}{space 3}0.988{col 67}{space 4} .6092103{col 80}{space 3} 1.653805
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0012303{col 39}{space 2} .0012151{col 50}{space 1}   -6.78{col 59}{space 3}0.000{col 67}{space 4} .0001776{col 80}{space 3} .0085248
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.868079{col 39}{space 2}  .773149{col 67}{space 4} .8300515{col 80}{space 3} 4.204218
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_higher if p_green_vs_mainstream==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-595.57657}  
Iteration 1:{space 3}log likelihood = {res:-594.89964}  
Iteration 2:{space 3}log likelihood = {res:-594.89944}  
Iteration 3:{space 3}log likelihood = {res:-594.89944}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-605.82187}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-605.82187}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-595.22736}  
Iteration 2:{space 3}log pseudolikelihood = {res:-595.01024}  
Iteration 3:{space 3}log pseudolikelihood = {res:-595.00768}  (backed up)
Iteration 4:{space 3}log pseudolikelihood = {res: -595.0064}  (backed up)
Iteration 5:{space 3}log pseudolikelihood = {res:-595.00608}  (backed up)
Iteration 6:{space 3}log pseudolikelihood = {res:  -595.006}  (backed up)
Iteration 7:{space 3}log pseudolikelihood = {res:-595.00599}  (backed up)
Iteration 8:{space 3}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 9:{space 3}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 17:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 23:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 25:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 33:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 34:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 35:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 36:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res:-595.00598}  (not concave)
Iteration 38:{space 2}log pseudolikelihood = {res:-595.00598}  (backed up)
Iteration 39:{space 2}log pseudolikelihood = {res:-595.00597}  (not concave)
Iteration 40:{space 2}log pseudolikelihood = {res:-595.00597}  (not concave)
Iteration 41:{space 2}log pseudolikelihood = {res:-595.00597}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res:-595.00595}  
Iteration 43:{space 2}log pseudolikelihood = {res:-595.00554}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res:-595.00546}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res:-595.00519}  
Iteration 46:{space 2}log pseudolikelihood = {res:-594.99873}  
Iteration 47:{space 2}log pseudolikelihood = {res:-594.98678}  
Iteration 48:{space 2}log pseudolikelihood = {res:-594.98408}  (backed up)
Iteration 49:{space 2}log pseudolikelihood = {res:-594.94725}  (not concave)
Iteration 50:{space 2}log pseudolikelihood = {res:-594.90804}  (not concave)
Iteration 51:{space 2}log pseudolikelihood = {res:-594.90804}  (not concave)
Iteration 52:{space 2}log pseudolikelihood = {res:-594.90785}  (not concave)
Iteration 53:{space 2}log pseudolikelihood = {res:-594.90781}  (not concave)
Iteration 54:{space 2}log pseudolikelihood = {res:-594.90075}  
Iteration 55:{space 2}log pseudolikelihood = {res:-594.90071}  (backed up)
Iteration 56:{space 2}log pseudolikelihood = {res:-594.89944}  
Iteration 57:{space 2}log pseudolikelihood = {res:-594.89944}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,045
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        21

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      49.8
{col 63}{txt}max{col 67}={res}{col 69}       177

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   141.75
{txt}Log pseudolikelihood = {res}-594.89944{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:21} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .7970155{col 39}{space 2} .1029144{col 50}{space 1}   -1.76{col 59}{space 3}0.079{col 67}{space 4} .6188075{col 80}{space 3} 1.026545
{txt}{space 22}age {c |}{col 27}{res}{space 2} 1.000959{col 39}{space 2} .0063561{col 50}{space 1}    0.15{col 59}{space 3}0.880{col 67}{space 4} .9885786{col 80}{space 3} 1.013495
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .9535357{col 39}{space 2}  .275517{col 50}{space 1}   -0.16{col 59}{space 3}0.869{col 67}{space 4} .5412385{col 80}{space 3} 1.679907
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8601778{col 39}{space 2} .2955946{col 50}{space 1}   -0.44{col 59}{space 3}0.661{col 67}{space 4} .4386098{col 80}{space 3} 1.686934
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.206799{col 39}{space 2} .2021017{col 50}{space 1}    1.12{col 59}{space 3}0.262{col 67}{space 4} .8691301{col 80}{space 3} 1.675656
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9458185{col 39}{space 2} .1273988{col 50}{space 1}   -0.41{col 59}{space 3}0.679{col 67}{space 4} .7263629{col 80}{space 3} 1.231578
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9907063{col 39}{space 2} .1822808{col 50}{space 1}   -0.05{col 59}{space 3}0.960{col 67}{space 4} .6907672{col 80}{space 3} 1.420883
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.252345{col 39}{space 2}   .07906{col 50}{space 1}    3.56{col 59}{space 3}0.000{col 67}{space 4} 1.106593{col 80}{space 3} 1.417294
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5548944{col 39}{space 2} .0961219{col 50}{space 1}   -3.40{col 59}{space 3}0.001{col 67}{space 4} .3951486{col 80}{space 3} .7792203
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .9511968{col 39}{space 2} .1672571{col 50}{space 1}   -0.28{col 59}{space 3}0.776{col 67}{space 4} .6739013{col 80}{space 3} 1.342593
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.010538{col 39}{space 2} .0104711{col 50}{space 1}    1.01{col 59}{space 3}0.312{col 67}{space 4} .9902216{col 80}{space 3} 1.031271
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9268396{col 39}{space 2} .0319545{col 50}{space 1}   -2.20{col 59}{space 3}0.028{col 67}{space 4} .8662792{col 80}{space 3} .9916337
{txt}{space 14}lpss_higher {c |}{col 27}{res}{space 2} 1.090781{col 39}{space 2} .0773161{col 50}{space 1}    1.23{col 59}{space 3}0.220{col 67}{space 4} .9492997{col 80}{space 3} 1.253349
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .5963183{col 39}{space 2} .2373627{col 50}{space 1}   -1.30{col 59}{space 3}0.194{col 67}{space 4} .2733128{col 80}{space 3} 1.301057
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 4.28e-33{col 39}{space 2} 7.89e-33{col 67}{space 4} 1.16e-34{col 80}{space 3} 1.58e-31
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}21
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea14.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A14. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea14.rtf"'})

{com}. 
. *************
. **Table A15**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lenep_new if p_niche==0 & mainniche2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-7312.8846}  
Iteration 1:{space 3}log likelihood = {res:-6924.9355}  
Iteration 2:{space 3}log likelihood = {res:-6922.8196}  
Iteration 3:{space 3}log likelihood = {res:-6922.8163}  
Iteration 4:{space 3}log likelihood = {res:-6922.8163}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -6795.706}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -6795.706}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6791.2521}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6785.1869}  
Iteration 3:{space 3}log pseudolikelihood = {res:-6771.2615}  
Iteration 4:{space 3}log pseudolikelihood = {res: -6770.956}  
Iteration 5:{space 3}log pseudolikelihood = {res:-6770.9514}  
Iteration 6:{space 3}log pseudolikelihood = {res:-6770.9514}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,872
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}     663.4
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   422.77
{txt}Log pseudolikelihood = {res}-6770.9514{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.084781{col 39}{space 2} .0693025{col 50}{space 1}    1.27{col 59}{space 3}0.203{col 67}{space 4} .9571106{col 80}{space 3} 1.229482
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9846161{col 39}{space 2} .0025237{col 50}{space 1}   -6.05{col 59}{space 3}0.000{col 67}{space 4} .9796822{col 80}{space 3} .9895749
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.275424{col 39}{space 2}  .120035{col 50}{space 1}    2.58{col 59}{space 3}0.010{col 67}{space 4} 1.060583{col 80}{space 3} 1.533785
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.380068{col 39}{space 2} .1731541{col 50}{space 1}    2.57{col 59}{space 3}0.010{col 67}{space 4}   1.0792{col 80}{space 3} 1.764813
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8139884{col 39}{space 2} .0556708{col 50}{space 1}   -3.01{col 59}{space 3}0.003{col 67}{space 4} .7118726{col 80}{space 3} .9307523
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6442995{col 39}{space 2} .0596181{col 50}{space 1}   -4.75{col 59}{space 3}0.000{col 67}{space 4} .5374335{col 80}{space 3} .7724153
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.102972{col 39}{space 2} .1769232{col 50}{space 1}    8.84{col 59}{space 3}0.000{col 67}{space 4} 1.783289{col 80}{space 3} 2.479962
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9739601{col 39}{space 2}  .034068{col 50}{space 1}   -0.75{col 59}{space 3}0.451{col 67}{space 4} .9094255{col 80}{space 3} 1.043074
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3948376{col 39}{space 2} .0406092{col 50}{space 1}   -9.04{col 59}{space 3}0.000{col 67}{space 4} .3227544{col 80}{space 3} .4830197
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.217017{col 39}{space 2} .2407821{col 50}{space 1}    0.99{col 59}{space 3}0.321{col 67}{space 4} .8258278{col 80}{space 3} 1.793509
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.004617{col 39}{space 2} .0109461{col 50}{space 1}    0.42{col 59}{space 3}0.672{col 67}{space 4} .9833902{col 80}{space 3} 1.026302
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2}  1.01195{col 39}{space 2} .0095637{col 50}{space 1}    1.26{col 59}{space 3}0.209{col 67}{space 4} .9933785{col 80}{space 3} 1.030869
{txt}{space 16}lenep_new {c |}{col 27}{res}{space 2} 1.509475{col 39}{space 2} .1989735{col 50}{space 1}    3.12{col 59}{space 3}0.002{col 67}{space 4} 1.165799{col 80}{space 3} 1.954467
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0194937{col 39}{space 2} .0118132{col 50}{space 1}   -6.50{col 59}{space 3}0.000{col 67}{space 4} .0059438{col 80}{space 3} .0639326
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .3802725{col 39}{space 2} .1424508{col 67}{space 4} .1824876{col 80}{space 3} .7924222
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lenep_new if p_niche==1 & nichemain2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2257.1137}  
Iteration 1:{space 3}log likelihood = {res:-2251.0756}  
Iteration 2:{space 3}log likelihood = {res:-2251.0694}  
Iteration 3:{space 3}log likelihood = {res:-2251.0694}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2243.9621}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2243.9621}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2237.3527}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2236.0217}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2235.0653}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2235.0026}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2235.0021}  
Iteration 6:{space 3}log pseudolikelihood = {res:-2235.0021}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,515
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     141.1
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   228.93
{txt}Log pseudolikelihood = {res}-2235.0021{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:32} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8234308{col 39}{space 2} .0882646{col 50}{space 1}   -1.81{col 59}{space 3}0.070{col 67}{space 4} .6673993{col 80}{space 3} 1.015941
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9933253{col 39}{space 2} .0027969{col 50}{space 1}   -2.38{col 59}{space 3}0.017{col 67}{space 4} .9878586{col 80}{space 3} .9988222
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.307642{col 39}{space 2} .1605659{col 50}{space 1}    2.18{col 59}{space 3}0.029{col 67}{space 4} 1.027944{col 80}{space 3} 1.663444
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.052263{col 39}{space 2} .1462055{col 50}{space 1}    0.37{col 59}{space 3}0.714{col 67}{space 4} .8014101{col 80}{space 3} 1.381635
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.121523{col 39}{space 2} .1150347{col 50}{space 1}    1.12{col 59}{space 3}0.264{col 67}{space 4} .9172767{col 80}{space 3} 1.371248
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.603051{col 39}{space 2} .1867445{col 50}{space 1}    4.05{col 59}{space 3}0.000{col 67}{space 4} 1.275817{col 80}{space 3} 2.014219
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .7908075{col 39}{space 2} .0661185{col 50}{space 1}   -2.81{col 59}{space 3}0.005{col 67}{space 4} .6712786{col 80}{space 3} .9316199
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.118535{col 39}{space 2} .0416303{col 50}{space 1}    3.01{col 59}{space 3}0.003{col 67}{space 4} 1.039846{col 80}{space 3} 1.203178
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4270747{col 39}{space 2} .0406973{col 50}{space 1}   -8.93{col 59}{space 3}0.000{col 67}{space 4} .3543155{col 80}{space 3} .5147752
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7556343{col 39}{space 2} .1446482{col 50}{space 1}   -1.46{col 59}{space 3}0.143{col 67}{space 4} .5192415{col 80}{space 3} 1.099649
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9984403{col 39}{space 2}  .012324{col 50}{space 1}   -0.13{col 59}{space 3}0.899{col 67}{space 4} .9745755{col 80}{space 3} 1.022889
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9999548{col 39}{space 2} .0100022{col 50}{space 1}   -0.00{col 59}{space 3}0.996{col 67}{space 4} .9805418{col 80}{space 3} 1.019752
{txt}{space 16}lenep_new {c |}{col 27}{res}{space 2} .8216292{col 39}{space 2} .0936814{col 50}{space 1}   -1.72{col 59}{space 3}0.085{col 67}{space 4} .6570866{col 80}{space 3} 1.027375
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 1.148207{col 39}{space 2}  .557905{col 50}{space 1}    0.28{col 59}{space 3}0.776{col 67}{space 4} .4430245{col 80}{space 3}  2.97586
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1743366{col 39}{space 2} .1436861{col 67}{space 4} .0346608{col 80}{space 3} .8768768
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}32
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lenep_new if p_radicalrl_vs_mainstream==0 & mainradical2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5436.6146}  
Iteration 1:{space 3}log likelihood = {res:-4756.3302}  
Iteration 2:{space 3}log likelihood = {res:-4746.3124}  
Iteration 3:{space 3}log likelihood = {res: -4746.268}  
Iteration 4:{space 3}log likelihood = {res: -4746.268}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4446.1233}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4446.1233}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4421.4365}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4418.9288}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4418.7377}  
Iteration 4:{space 3}log pseudolikelihood = {res: -4418.734}  
Iteration 5:{space 3}log pseudolikelihood = {res: -4418.734}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,042
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     642.1
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   831.11
{txt}Log pseudolikelihood = {res}-4418.734{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.314789{col 40}{space 2} .0916985{col 51}{space 1}    3.92{col 60}{space 3}0.000{col 68}{space 4} 1.146806{col 81}{space 3} 1.507378
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9875457{col 40}{space 2} .0030081{col 51}{space 1}   -4.11{col 60}{space 3}0.000{col 68}{space 4} .9816675{col 81}{space 3} .9934592
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.367384{col 40}{space 2} .1858453{col 51}{space 1}    2.30{col 60}{space 3}0.021{col 68}{space 4} 1.047614{col 81}{space 3}  1.78476
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.150148{col 40}{space 2} .1846051{col 51}{space 1}    0.87{col 60}{space 3}0.383{col 68}{space 4} .8397135{col 81}{space 3} 1.575347
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.708548{col 40}{space 2} .2619292{col 51}{space 1}   10.30{col 60}{space 3}0.000{col 68}{space 4} 2.240894{col 81}{space 3} 3.273796
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7412182{col 40}{space 2} .0604286{col 51}{space 1}   -3.67{col 60}{space 3}0.000{col 68}{space 4} .6317584{col 81}{space 3} .8696434
{txt}{space 14}High income  {c |}{col 28}{res}{space 2}   .54433{col 40}{space 2} .0518728{col 51}{space 1}   -6.38{col 60}{space 3}0.000{col 68}{space 4} .4515913{col 81}{space 3} .6561135
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.037379{col 40}{space 2}   .04209{col 51}{space 1}    0.90{col 60}{space 3}0.366{col 68}{space 4} .9580788{col 81}{space 3} 1.123242
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3516315{col 40}{space 2} .0457422{col 51}{space 1}   -8.03{col 60}{space 3}0.000{col 68}{space 4}  .272495{col 81}{space 3} .4537503
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.159264{col 40}{space 2} .2659055{col 51}{space 1}    0.64{col 60}{space 3}0.519{col 68}{space 4} .7395005{col 81}{space 3} 1.817298
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9365019{col 40}{space 2} .0262487{col 51}{space 1}   -2.34{col 60}{space 3}0.019{col 68}{space 4} .8864429{col 81}{space 3} .9893878
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 1.080172{col 40}{space 2} .0392037{col 51}{space 1}    2.12{col 60}{space 3}0.034{col 68}{space 4} 1.006003{col 81}{space 3} 1.159809
{txt}{space 17}lenep_new {c |}{col 28}{res}{space 2} 2.100893{col 40}{space 2} .8205043{col 51}{space 1}    1.90{col 60}{space 3}0.057{col 68}{space 4} .9771625{col 81}{space 3} 4.516905
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0020747{col 40}{space 2} .0031633{col 51}{space 1}   -4.05{col 60}{space 3}0.000{col 68}{space 4} .0001045{col 81}{space 3} .0411875
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 2.055575{col 40}{space 2} .9162556{col 68}{space 4} .8580596{col 81}{space 3} 4.924355
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lenep_new if p_radicalrl_vs_mainstream==1 & radicalmain2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1323.0197}  
Iteration 1:{space 3}log likelihood = {res:-1317.7012}  
Iteration 2:{space 3}log likelihood = {res:-1317.6854}  
Iteration 3:{space 3}log likelihood = {res:-1317.6854}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1292.2712}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1292.2712}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1290.0052}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1286.4088}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1286.3388}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1286.3384}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1286.3384}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,716
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     100.6
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   453.56
{txt}Log pseudolikelihood = {res}-1286.3384{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:27} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8885044{col 40}{space 2} .1337344{col 51}{space 1}   -0.79{col 60}{space 3}0.432{col 68}{space 4} .6615153{col 81}{space 3} 1.193381
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9927968{col 40}{space 2} .0037642{col 51}{space 1}   -1.91{col 60}{space 3}0.057{col 68}{space 4} .9854464{col 81}{space 3} 1.000202
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.160166{col 40}{space 2} .2011703{col 51}{space 1}    0.86{col 60}{space 3}0.392{col 68}{space 4} .8258926{col 81}{space 3} 1.629734
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7524374{col 40}{space 2} .1533758{col 51}{space 1}   -1.40{col 60}{space 3}0.163{col 68}{space 4} .5046178{col 81}{space 3} 1.121962
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .6629998{col 40}{space 2} .0520963{col 51}{space 1}   -5.23{col 60}{space 3}0.000{col 68}{space 4}  .568367{col 81}{space 3} .7733888
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.100705{col 40}{space 2} .1837154{col 51}{space 1}    0.57{col 60}{space 3}0.565{col 68}{space 4} .7935955{col 81}{space 3}  1.52666
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.758703{col 40}{space 2} .2794415{col 51}{space 1}    3.55{col 60}{space 3}0.000{col 68}{space 4} 1.288085{col 81}{space 3} 2.401268
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2}  1.05907{col 40}{space 2} .0522526{col 51}{space 1}    1.16{col 60}{space 3}0.245{col 68}{space 4} .9614526{col 81}{space 3} 1.166599
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3907239{col 40}{space 2} .0424467{col 51}{space 1}   -8.65{col 60}{space 3}0.000{col 68}{space 4} .3157903{col 81}{space 3} .4834383
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.345353{col 40}{space 2} .4226508{col 51}{space 1}    0.94{col 60}{space 3}0.345{col 68}{space 4} .7268187{col 81}{space 3} 2.490269
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9783395{col 40}{space 2} .0195934{col 51}{space 1}   -1.09{col 60}{space 3}0.274{col 68}{space 4}  .940681{col 81}{space 3} 1.017506
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9870248{col 40}{space 2} .0183885{col 51}{space 1}   -0.70{col 60}{space 3}0.483{col 68}{space 4}  .951634{col 81}{space 3} 1.023732
{txt}{space 17}lenep_new {c |}{col 28}{res}{space 2} .8918475{col 40}{space 2} .1727484{col 51}{space 1}   -0.59{col 60}{space 3}0.555{col 68}{space 4} .6101199{col 81}{space 3} 1.303665
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 1.490068{col 40}{space 2} 1.390283{col 51}{space 1}    0.43{col 60}{space 3}0.669{col 68}{space 4} .2393353{col 81}{space 3} 9.276955
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .4479496{col 40}{space 2}  .166727{col 68}{space 4}  .215979{col 81}{space 3} .9290661
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}27
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lenep_new if p_green_vs_mainstream==0 & maingreen2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3538.7624}  
Iteration 1:{space 3}log likelihood = {res:-2398.5435}  
Iteration 2:{space 3}log likelihood = {res:-2322.2051}  
Iteration 3:{space 3}log likelihood = {res:-2315.7349}  
Iteration 4:{space 3}log likelihood = {res:-2315.6845}  
Iteration 5:{space 3}log likelihood = {res:-2315.6845}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2255.9281}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2255.9281}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2248.7938}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2245.1052}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2244.5927}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2244.5687}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2244.5686}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,275
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        70
{col 63}{txt}avg{col 67}={res}{col 69}     622.4
{col 63}{txt}max{col 67}={res}{col 69}     1,256

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   406.89
{txt}Log pseudolikelihood = {res}-2244.5686{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .6797645{col 39}{space 2} .0504501{col 50}{space 1}   -5.20{col 59}{space 3}0.000{col 67}{space 4} .5877394{col 80}{space 3} .7861983
{txt}{space 22}age {c |}{col 27}{res}{space 2}  .978731{col 39}{space 2} .0037059{col 50}{space 1}   -5.68{col 59}{space 3}0.000{col 67}{space 4} .9714945{col 80}{space 3} .9860215
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.744764{col 39}{space 2}  .474086{col 50}{space 1}    2.05{col 59}{space 3}0.041{col 67}{space 4} 1.024351{col 80}{space 3} 2.971834
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 3.329986{col 39}{space 2} 1.153844{col 50}{space 1}    3.47{col 59}{space 3}0.001{col 67}{space 4} 1.688496{col 80}{space 3} 6.567269
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.153272{col 39}{space 2} .1577309{col 50}{space 1}    1.04{col 59}{space 3}0.297{col 67}{space 4}  .882093{col 80}{space 3} 1.507818
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.041073{col 39}{space 2} .1387474{col 50}{space 1}    0.30{col 59}{space 3}0.763{col 67}{space 4} .8017497{col 80}{space 3} 1.351836
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9490406{col 39}{space 2} .1892936{col 50}{space 1}   -0.26{col 59}{space 3}0.793{col 67}{space 4}  .641957{col 80}{space 3} 1.403019
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9126094{col 39}{space 2} .0423773{col 50}{space 1}   -1.97{col 59}{space 3}0.049{col 67}{space 4} .8332188{col 80}{space 3} .9995643
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5370539{col 39}{space 2} .0562138{col 50}{space 1}   -5.94{col 59}{space 3}0.000{col 67}{space 4} .4374435{col 80}{space 3} .6593466
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.505406{col 39}{space 2} .4255292{col 50}{space 1}    1.45{col 59}{space 3}0.148{col 67}{space 4} .8650612{col 80}{space 3} 2.619754
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9991528{col 39}{space 2} .0324296{col 50}{space 1}   -0.03{col 59}{space 3}0.979{col 67}{space 4} .9375714{col 80}{space 3} 1.064779
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.735688{col 39}{space 2} .1784133{col 50}{space 1}    5.36{col 59}{space 3}0.000{col 67}{space 4} 1.418978{col 80}{space 3} 2.123086
{txt}{space 16}lenep_new {c |}{col 27}{res}{space 2}  .818835{col 39}{space 2} .2693987{col 50}{space 1}   -0.61{col 59}{space 3}0.544{col 67}{space 4} .4296839{col 80}{space 3} 1.560428
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0028222{col 39}{space 2} .0044517{col 50}{space 1}   -3.72{col 59}{space 3}0.000{col 67}{space 4} .0001282{col 80}{space 3} .0621207
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.790486{col 39}{space 2}  .730234{col 67}{space 4} .8050374{col 80}{space 3} 3.982227
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lenep_new if p_green_vs_mainstream==1 & greenmain2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-596.45948}  
Iteration 1:{space 3}log likelihood = {res:-595.75664}  
Iteration 2:{space 3}log likelihood = {res:-595.75644}  
Iteration 3:{space 3}log likelihood = {res:-595.75644}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-605.91769}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-605.91769}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-596.02363}  
Iteration 2:{space 3}log pseudolikelihood = {res:-596.00111}  
Iteration 3:{space 3}log pseudolikelihood = {res:-595.99065}  (backed up)
Iteration 4:{space 3}log pseudolikelihood = {res:-595.98561}  (backed up)
Iteration 5:{space 3}log pseudolikelihood = {res:-595.98314}  (backed up)
Iteration 6:{space 3}log pseudolikelihood = {res:-595.98283}  (backed up)
Iteration 7:{space 3}log pseudolikelihood = {res:-595.98276}  (backed up)
Iteration 8:{space 3}log pseudolikelihood = {res:-595.98272}  (backed up)
Iteration 9:{space 3}log pseudolikelihood = {res:-595.98271}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res: -595.9827}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res: -595.9827}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res: -595.9827}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res: -595.9827}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res: -595.9827}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res: -595.9827}  (backed up)
Iteration 16:{space 2}log pseudolikelihood = {res: -595.9827}  (backed up)
Iteration 17:{space 2}log pseudolikelihood = {res: -595.9827}  (backed up)
Iteration 18:{space 2}log pseudolikelihood = {res: -595.9827}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-595.94775}  
Iteration 20:{space 2}log pseudolikelihood = {res:-595.82789}  
Iteration 21:{space 2}log pseudolikelihood = {res:-595.81321}  
Iteration 22:{space 2}log pseudolikelihood = {res:-595.80682}  
Iteration 23:{space 2}log pseudolikelihood = {res:-595.80384}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-595.80239}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-595.80204}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:-595.80186}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 31:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 34:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 40:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 41:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 43:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 44:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 45:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 47:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 48:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 50:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 51:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 52:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 53:{space 2}log pseudolikelihood = {res:-595.80184}  (backed up)
Iteration 54:{space 2}log pseudolikelihood = {res:-595.80184}  (not concave)
Iteration 55:{space 2}log pseudolikelihood = {res:-595.80184}  
Iteration 56:{space 2}log pseudolikelihood = {res:-595.80183}  (backed up)
Iteration 57:{space 2}log pseudolikelihood = {res:-595.80182}  (backed up)
Iteration 58:{space 2}log pseudolikelihood = {res:-595.80178}  (backed up)
Iteration 59:{space 2}log pseudolikelihood = {res:-595.80173}  (backed up)
Iteration 60:{space 2}log pseudolikelihood = {res:-595.80173}  (not concave)
Iteration 61:{space 2}log pseudolikelihood = {res:-595.80168}  
Iteration 62:{space 2}log pseudolikelihood = {res:-595.80133}  (not concave)
Iteration 63:{space 2}log pseudolikelihood = {res:-595.80131}  
Iteration 64:{space 2}log pseudolikelihood = {res:-595.79992}  (backed up)
Iteration 65:{space 2}log pseudolikelihood = {res:-595.78097}  
Iteration 66:{space 2}log pseudolikelihood = {res:-595.76261}  
Iteration 67:{space 2}log pseudolikelihood = {res:-595.75799}  (not concave)
Iteration 68:{space 2}log pseudolikelihood = {res:-595.75799}  (not concave)
Iteration 69:{space 2}log pseudolikelihood = {res:-595.75789}  (not concave)
Iteration 70:{space 2}log pseudolikelihood = {res:-595.75782}  
Iteration 71:{space 2}log pseudolikelihood = {res:-595.75644}  
Iteration 72:{space 2}log pseudolikelihood = {res:-595.75644}  (not concave)
Iteration 73:{space 2}log pseudolikelihood = {res:-595.75644}  (not concave)
Iteration 74:{space 2}log pseudolikelihood = {res:-595.75644}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,045
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        21

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      49.8
{col 63}{txt}max{col 67}={res}{col 69}       177

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   342.78
{txt}Log pseudolikelihood = {res}-595.75644{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:21} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .7925869{col 39}{space 2} .1029096{col 50}{space 1}   -1.79{col 59}{space 3}0.073{col 67}{space 4} .6145069{col 80}{space 3} 1.022273
{txt}{space 22}age {c |}{col 27}{res}{space 2} 1.000548{col 39}{space 2} .0063012{col 50}{space 1}    0.09{col 59}{space 3}0.931{col 67}{space 4} .9882738{col 80}{space 3} 1.012975
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .9622302{col 39}{space 2} .2861129{col 50}{space 1}   -0.13{col 59}{space 3}0.897{col 67}{space 4} .5372541{col 80}{space 3} 1.723369
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8446821{col 39}{space 2} .2914617{col 50}{space 1}   -0.49{col 59}{space 3}0.625{col 67}{space 4} .4295187{col 80}{space 3} 1.661133
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.211939{col 39}{space 2} .1986338{col 50}{space 1}    1.17{col 59}{space 3}0.241{col 67}{space 4} .8789632{col 80}{space 3} 1.671054
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9621145{col 39}{space 2} .1251297{col 50}{space 1}   -0.30{col 59}{space 3}0.766{col 67}{space 4} .7456278{col 80}{space 3} 1.241456
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.019011{col 39}{space 2} .1839419{col 50}{space 1}    0.10{col 59}{space 3}0.917{col 67}{space 4} .7153662{col 80}{space 3} 1.451542
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.259922{col 39}{space 2} .0792278{col 50}{space 1}    3.67{col 59}{space 3}0.000{col 67}{space 4} 1.113826{col 80}{space 3} 1.425181
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5568028{col 39}{space 2} .0979123{col 50}{space 1}   -3.33{col 59}{space 3}0.001{col 67}{space 4} .3944753{col 80}{space 3} .7859284
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .9067851{col 39}{space 2} .1812779{col 50}{space 1}   -0.49{col 59}{space 3}0.625{col 67}{space 4} .6128276{col 80}{space 3} 1.341746
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.004534{col 39}{space 2} .0093666{col 50}{space 1}    0.49{col 59}{space 3}0.628{col 67}{space 4}  .986343{col 80}{space 3} 1.023061
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9364026{col 39}{space 2} .0283778{col 50}{space 1}   -2.17{col 59}{space 3}0.030{col 67}{space 4} .8824028{col 80}{space 3}  .993707
{txt}{space 16}lenep_new {c |}{col 27}{res}{space 2} 1.001812{col 39}{space 2} .0600893{col 50}{space 1}    0.03{col 59}{space 3}0.976{col 67}{space 4} .8906987{col 80}{space 3} 1.126787
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .6148188{col 39}{space 2} .3282445{col 50}{space 1}   -0.91{col 59}{space 3}0.362{col 67}{space 4} .2159238{col 80}{space 3} 1.750627
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 7.35e-35{col 39}{space 2} 3.84e-35{col 67}{space 4} 2.64e-35{col 80}{space 3} 2.05e-34
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}21
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea15.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A15. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea15.rtf"'})

{com}. 
. *************
. **Table A16**
. ************* 
. 
. melogit c_niche male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lenep_new if p_niche==0 & mainniche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-11427.707}  
Iteration 1:{space 3}log likelihood = {res:-10637.173}  
Iteration 2:{space 3}log likelihood = {res:-10624.451}  
Iteration 3:{space 3}log likelihood = {res:-10624.398}  
Iteration 4:{space 3}log likelihood = {res:-10624.398}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-10235.106}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-10235.106}  
Iteration 1:{space 3}log pseudolikelihood = {res:-10231.497}  (backed up)
Iteration 2:{space 3}log pseudolikelihood = {res: -10202.19}  
Iteration 3:{space 3}log pseudolikelihood = {res: -10193.38}  
Iteration 4:{space 3}log pseudolikelihood = {res: -10193.35}  
Iteration 5:{space 3}log pseudolikelihood = {res: -10193.35}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    44,930
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        53

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        81
{col 63}{txt}avg{col 67}={res}{col 69}     847.7
{col 63}{txt}max{col 67}={res}{col 69}     1,953

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   612.65
{txt}Log pseudolikelihood = {res}-10193.35{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:53} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.049356{col 39}{space 2} .0534551{col 50}{space 1}    0.95{col 59}{space 3}0.344{col 67}{space 4} .9496465{col 80}{space 3} 1.159535
{txt}{space 22}age {c |}{col 27}{res}{space 2}  .983911{col 39}{space 2} .0019438{col 50}{space 1}   -8.21{col 59}{space 3}0.000{col 67}{space 4} .9801085{col 80}{space 3} .9877282
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.222683{col 39}{space 2} .0983656{col 50}{space 1}    2.50{col 59}{space 3}0.012{col 67}{space 4} 1.044322{col 80}{space 3} 1.431508
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  1.30392{col 39}{space 2} .1227455{col 50}{space 1}    2.82{col 59}{space 3}0.005{col 67}{space 4} 1.084233{col 80}{space 3} 1.568121
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8295851{col 39}{space 2} .0510739{col 50}{space 1}   -3.03{col 59}{space 3}0.002{col 67}{space 4} .7352858{col 80}{space 3}  .935978
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6289699{col 39}{space 2} .0502297{col 50}{space 1}   -5.81{col 59}{space 3}0.000{col 67}{space 4} .5378394{col 80}{space 3} .7355414
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9963484{col 39}{space 2}  .036337{col 50}{space 1}   -0.10{col 59}{space 3}0.920{col 67}{space 4} .9276151{col 80}{space 3} 1.070175
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3904293{col 39}{space 2} .0296738{col 50}{space 1}  -12.37{col 59}{space 3}0.000{col 67}{space 4} .3363943{col 80}{space 3}  .453144
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.234675{col 39}{space 2} .2182693{col 50}{space 1}    1.19{col 59}{space 3}0.233{col 67}{space 4} .8731222{col 80}{space 3} 1.745945
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.000961{col 39}{space 2} .0114361{col 50}{space 1}    0.08{col 59}{space 3}0.933{col 67}{space 4} .9787954{col 80}{space 3} 1.023628
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2}  1.00243{col 39}{space 2} .0113215{col 50}{space 1}    0.21{col 59}{space 3}0.830{col 67}{space 4}  .980484{col 80}{space 3} 1.024867
{txt}{space 16}lenep_new {c |}{col 27}{res}{space 2} 1.576745{col 39}{space 2} .2371297{col 50}{space 1}    3.03{col 59}{space 3}0.002{col 67}{space 4} 1.174215{col 80}{space 3} 2.117265
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0228953{col 39}{space 2} .0140587{col 50}{space 1}   -6.15{col 59}{space 3}0.000{col 67}{space 4} .0068719{col 80}{space 3} .0762815
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .5790901{col 39}{space 2} .1382213{col 67}{space 4} .3627232{col 80}{space 3} .9245213
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}53
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lenep_new if p_niche==1 & nichemain==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3362.5494}  
Iteration 1:{space 3}log likelihood = {res:-3356.3027}  
Iteration 2:{space 3}log likelihood = {res:-3356.3002}  
Iteration 3:{space 3}log likelihood = {res:-3356.3002}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-3348.2503}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-3348.2503}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-3337.8336}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-3333.8585}  
Iteration 3:{space 3}log pseudolikelihood = {res:-3332.9363}  
Iteration 4:{space 3}log pseudolikelihood = {res:-3332.8319}  
Iteration 5:{space 3}log pseudolikelihood = {res:-3332.8319}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     6,473
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        45

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     143.8
{col 63}{txt}max{col 67}={res}{col 69}       421

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   237.90
{txt}Log pseudolikelihood = {res}-3332.8319{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:45} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8936324{col 39}{space 2} .0678265{col 50}{space 1}   -1.48{col 59}{space 3}0.138{col 67}{space 4} .7701102{col 80}{space 3} 1.036967
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9913759{col 39}{space 2}  .002551{col 50}{space 1}   -3.37{col 59}{space 3}0.001{col 67}{space 4} .9863885{col 80}{space 3} .9963884
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.111824{col 39}{space 2} .1006715{col 50}{space 1}    1.17{col 59}{space 3}0.242{col 67}{space 4} .9310281{col 80}{space 3} 1.327728
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8644804{col 39}{space 2} .0860696{col 50}{space 1}   -1.46{col 59}{space 3}0.144{col 67}{space 4}  .711226{col 80}{space 3} 1.050758
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.167613{col 39}{space 2} .1005605{col 50}{space 1}    1.80{col 59}{space 3}0.072{col 67}{space 4} .9862553{col 80}{space 3}  1.38232
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.672541{col 39}{space 2} .1762709{col 50}{space 1}    4.88{col 59}{space 3}0.000{col 67}{space 4} 1.360404{col 80}{space 3} 2.056297
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.163557{col 39}{space 2} .0445593{col 50}{space 1}    3.96{col 59}{space 3}0.000{col 67}{space 4} 1.079419{col 80}{space 3} 1.254253
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4492724{col 39}{space 2} .0357164{col 50}{space 1}  -10.06{col 59}{space 3}0.000{col 67}{space 4} .3844506{col 80}{space 3} .5250237
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7298884{col 39}{space 2} .1281581{col 50}{space 1}   -1.79{col 59}{space 3}0.073{col 67}{space 4} .5173655{col 80}{space 3} 1.029711
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9943998{col 39}{space 2} .0088022{col 50}{space 1}   -0.63{col 59}{space 3}0.526{col 67}{space 4} .9772966{col 80}{space 3} 1.011802
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} 1.001332{col 39}{space 2} .0075167{col 50}{space 1}    0.18{col 59}{space 3}0.859{col 67}{space 4} .9867075{col 80}{space 3} 1.016173
{txt}{space 16}lenep_new {c |}{col 27}{res}{space 2} .8791143{col 39}{space 2} .0654578{col 50}{space 1}   -1.73{col 59}{space 3}0.084{col 67}{space 4} .7597417{col 80}{space 3} 1.017243
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .8737865{col 39}{space 2} .3103059{col 50}{space 1}   -0.38{col 59}{space 3}0.704{col 67}{space 4} .4356322{col 80}{space 3} 1.752632
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1319455{col 39}{space 2} .0685916{col 67}{space 4}  .047632{col 80}{space 3} .3655024
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}45
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lenep_new if p_radicalrl_vs_mainstream==0 & mainradical==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-8961.9325}  
Iteration 1:{space 3}log likelihood = {res:-7833.8116}  
Iteration 2:{space 3}log likelihood = {res:-7798.8814}  
Iteration 3:{space 3}log likelihood = {res:-7798.4643}  
Iteration 4:{space 3}log likelihood = {res:-7798.4642}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-7249.6372}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-7249.6372}  
Iteration 1:{space 3}log pseudolikelihood = {res:-7211.1285}  
Iteration 2:{space 3}log pseudolikelihood = {res: -7208.415}  
Iteration 3:{space 3}log pseudolikelihood = {res:-7208.2699}  
Iteration 4:{space 3}log pseudolikelihood = {res:-7208.2686}  
Iteration 5:{space 3}log pseudolikelihood = {res:-7208.2686}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    43,867
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        53

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        79
{col 63}{txt}avg{col 67}={res}{col 69}     827.7
{col 63}{txt}max{col 67}={res}{col 69}     1,953

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   615.18
{txt}Log pseudolikelihood = {res}-7208.2686{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:53} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.191388{col 40}{space 2} .0752799{col 51}{space 1}    2.77{col 60}{space 3}0.006{col 68}{space 4} 1.052612{col 81}{space 3} 1.348459
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9847289{col 40}{space 2} .0023847{col 51}{space 1}   -6.35{col 60}{space 3}0.000{col 68}{space 4} .9800661{col 81}{space 3} .9894139
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.274338{col 40}{space 2} .1318536{col 51}{space 1}    2.34{col 60}{space 3}0.019{col 68}{space 4} 1.040429{col 81}{space 3} 1.560836
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.074088{col 40}{space 2} .1281089{col 51}{space 1}    0.60{col 60}{space 3}0.549{col 68}{space 4} .8501885{col 81}{space 3} 1.356952
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7632343{col 40}{space 2} .0513583{col 51}{space 1}   -4.02{col 60}{space 3}0.000{col 68}{space 4} .6689293{col 81}{space 3} .8708343
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5342941{col 40}{space 2} .0401138{col 51}{space 1}   -8.35{col 60}{space 3}0.000{col 68}{space 4} .4611835{col 81}{space 3} .6189948
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.056222{col 40}{space 2} .0455035{col 51}{space 1}    1.27{col 60}{space 3}0.204{col 68}{space 4} .9706986{col 81}{space 3} 1.149281
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3849806{col 40}{space 2} .0377228{col 51}{space 1}   -9.74{col 60}{space 3}0.000{col 68}{space 4} .3177114{col 81}{space 3} .4664926
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.228728{col 40}{space 2} .2668936{col 51}{space 1}    0.95{col 60}{space 3}0.343{col 68}{space 4} .8027218{col 81}{space 3} 1.880816
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2}  .953039{col 40}{space 2} .0231689{col 51}{space 1}   -1.98{col 60}{space 3}0.048{col 68}{space 4} .9086936{col 81}{space 3} .9995485
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2}  1.07467{col 40}{space 2} .0354526{col 51}{space 1}    2.18{col 60}{space 3}0.029{col 68}{space 4} 1.007383{col 81}{space 3} 1.146451
{txt}{space 17}lenep_new {c |}{col 28}{res}{space 2} 1.411703{col 40}{space 2}  .459402{col 51}{space 1}    1.06{col 60}{space 3}0.289{col 68}{space 4} .7460057{col 81}{space 3} 2.671436
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0175468{col 40}{space 2} .0220364{col 51}{space 1}   -3.22{col 60}{space 3}0.001{col 68}{space 4} .0014969{col 81}{space 3} .2056796
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 1.859018{col 40}{space 2} .6644624{col 68}{space 4} .9226596{col 81}{space 3} 3.745636
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}53
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lenep_new if p_radicalrl_vs_mainstream==1 & radicalmain==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2032.3108}  
Iteration 1:{space 3}log likelihood = {res:-2028.1231}  
Iteration 2:{space 3}log likelihood = {res:-2028.1209}  
Iteration 3:{space 3}log likelihood = {res:-2028.1209}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1995.0856}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1995.0856}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1989.8676}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1986.8751}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1985.3012}  
Iteration 4:{space 3}log pseudolikelihood = {res: -1985.296}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1985.2959}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     3,975
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        40

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}      99.4
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   295.08
{txt}Log pseudolikelihood = {res}-1985.2959{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:40} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .9343939{col 40}{space 2} .0949255{col 51}{space 1}   -0.67{col 60}{space 3}0.504{col 68}{space 4} .7656954{col 81}{space 3}  1.14026
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9896727{col 40}{space 2} .0031137{col 51}{space 1}   -3.30{col 60}{space 3}0.001{col 68}{space 4} .9835887{col 81}{space 3} .9957944
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.097817{col 40}{space 2} .1233969{col 51}{space 1}    0.83{col 60}{space 3}0.406{col 68}{space 4} .8807508{col 81}{space 3}  1.36838
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .6614741{col 40}{space 2} .0905692{col 51}{space 1}   -3.02{col 60}{space 3}0.003{col 68}{space 4} .5057853{col 81}{space 3} .8650865
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.180245{col 40}{space 2} .1564012{col 51}{space 1}    1.25{col 60}{space 3}0.211{col 68}{space 4} .9102789{col 81}{space 3} 1.530277
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.820775{col 40}{space 2} .2486944{col 51}{space 1}    4.39{col 60}{space 3}0.000{col 68}{space 4} 1.393134{col 81}{space 3} 2.379685
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.127118{col 40}{space 2} .0552594{col 51}{space 1}    2.44{col 60}{space 3}0.015{col 68}{space 4} 1.023852{col 81}{space 3} 1.240799
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .4417286{col 40}{space 2} .0414224{col 51}{space 1}   -8.71{col 60}{space 3}0.000{col 68}{space 4} .3675661{col 81}{space 3} .5308546
{txt}{space 14}p_government {c |}{col 28}{res}{space 2}  1.55192{col 40}{space 2} .5390985{col 51}{space 1}    1.27{col 60}{space 3}0.206{col 68}{space 4} .7855669{col 81}{space 3} 3.065883
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9764598{col 40}{space 2} .0135114{col 51}{space 1}   -1.72{col 60}{space 3}0.085{col 68}{space 4} .9503339{col 81}{space 3} 1.003304
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9873066{col 40}{space 2} .0151924{col 51}{space 1}   -0.83{col 60}{space 3}0.406{col 68}{space 4} .9579746{col 81}{space 3} 1.017537
{txt}{space 17}lenep_new {c |}{col 28}{res}{space 2} .8914195{col 40}{space 2} .1278101{col 51}{space 1}   -0.80{col 60}{space 3}0.423{col 68}{space 4} .6730362{col 81}{space 3} 1.180663
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 1.360122{col 40}{space 2} .8895887{col 51}{space 1}    0.47{col 60}{space 3}0.638{col 68}{space 4}  .377441{col 81}{space 3} 4.901249
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2}   .31846{col 40}{space 2}  .093725{col 68}{space 4} .1788711{col 81}{space 3} .5669822
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}40
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lenep_new if p_green_vs_mainstream==0 & maingreen==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5552.6031}  
Iteration 1:{space 3}log likelihood = {res:-3341.0475}  
Iteration 2:{space 3}log likelihood = {res:-3243.0084}  
Iteration 3:{space 3}log likelihood = {res:-3187.4138}  
Iteration 4:{space 3}log likelihood = {res:-3187.2922}  
Iteration 5:{space 3}log likelihood = {res:-3187.2922}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-3037.7175}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-3037.7175}  
Iteration 1:{space 3}log pseudolikelihood = {res:-3021.6099}  
Iteration 2:{space 3}log pseudolikelihood = {res:-3010.9276}  
Iteration 3:{space 3}log pseudolikelihood = {res:-3008.9503}  
Iteration 4:{space 3}log pseudolikelihood = {res:-3008.6373}  
Iteration 5:{space 3}log pseudolikelihood = {res:-3008.6284}  
Iteration 6:{space 3}log pseudolikelihood = {res:-3008.6284}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    42,555
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        53

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        71
{col 63}{txt}avg{col 67}={res}{col 69}     802.9
{col 63}{txt}max{col 67}={res}{col 69}     1,921

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   441.81
{txt}Log pseudolikelihood = {res}-3008.6284{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:53} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .704195{col 39}{space 2} .0520867{col 50}{space 1}   -4.74{col 59}{space 3}0.000{col 67}{space 4} .6091619{col 80}{space 3} .8140538
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9812178{col 39}{space 2} .0032989{col 50}{space 1}   -5.64{col 59}{space 3}0.000{col 67}{space 4} .9747734{col 80}{space 3} .9877049
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.209376{col 39}{space 2} .2349275{col 50}{space 1}    0.98{col 59}{space 3}0.328{col 67}{space 4} .8264397{col 80}{space 3} 1.769749
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 2.267201{col 39}{space 2} .4485267{col 50}{space 1}    4.14{col 59}{space 3}0.000{col 67}{space 4}  1.53849{col 80}{space 3} 3.341071
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.056889{col 39}{space 2} .1162143{col 50}{space 1}    0.50{col 59}{space 3}0.615{col 67}{space 4}  .851986{col 80}{space 3} 1.311072
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9755779{col 39}{space 2}  .169955{col 50}{space 1}   -0.14{col 59}{space 3}0.887{col 67}{space 4}  .693385{col 80}{space 3} 1.372617
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9265236{col 39}{space 2} .0410729{col 50}{space 1}   -1.72{col 59}{space 3}0.085{col 67}{space 4} .8494202{col 80}{space 3} 1.010626
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4480237{col 39}{space 2} .0526611{col 50}{space 1}   -6.83{col 59}{space 3}0.000{col 67}{space 4}  .355836{col 80}{space 3} .5640947
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.444007{col 39}{space 2}  .320727{col 50}{space 1}    1.65{col 59}{space 3}0.098{col 67}{space 4} .9343502{col 80}{space 3} 2.231664
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9993599{col 39}{space 2} .0397643{col 50}{space 1}   -0.02{col 59}{space 3}0.987{col 67}{space 4} .9243847{col 80}{space 3} 1.080416
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.985676{col 39}{space 2} .2073571{col 50}{space 1}    6.57{col 59}{space 3}0.000{col 67}{space 4} 1.618156{col 80}{space 3} 2.436668
{txt}{space 16}lenep_new {c |}{col 27}{res}{space 2} .8029044{col 39}{space 2} .3104757{col 50}{space 1}   -0.57{col 59}{space 3}0.570{col 67}{space 4} .3762808{col 80}{space 3}  1.71323
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0016821{col 39}{space 2} .0029238{col 50}{space 1}   -3.68{col 59}{space 3}0.000{col 67}{space 4} .0000558{col 80}{space 3} .0507423
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2}  2.90298{col 39}{space 2} .9658718{col 67}{space 4} 1.512288{col 80}{space 3} 5.572545
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}53
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lenep_new if p_green_vs_mainstream==1 & greenmain==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-769.07497}  
Iteration 1:{space 3}log likelihood = {res:-768.45453}  
Iteration 2:{space 3}log likelihood = {res: -768.4544}  
Iteration 3:{space 3}log likelihood = {res: -768.4544}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:  -778.253}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -778.253}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-773.46523}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-768.84788}  
Iteration 3:{space 3}log pseudolikelihood = {res:-767.92856}  
Iteration 4:{space 3}log pseudolikelihood = {res:-767.70683}  
Iteration 5:{space 3}log pseudolikelihood = {res: -767.7035}  
Iteration 6:{space 3}log pseudolikelihood = {res: -767.7035}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,320
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        25

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      52.8
{col 63}{txt}max{col 67}={res}{col 69}       205

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}   153.72
{txt}Log pseudolikelihood = {res}-767.7035{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:25} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8820804{col 39}{space 2} .0929753{col 50}{space 1}   -1.19{col 59}{space 3}0.234{col 67}{space 4} .7174434{col 80}{space 3} 1.084498
{txt}{space 22}age {c |}{col 27}{res}{space 2}  .999427{col 39}{space 2} .0053171{col 50}{space 1}   -0.11{col 59}{space 3}0.914{col 67}{space 4} .9890598{col 80}{space 3} 1.009903
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .8488749{col 39}{space 2} .1877361{col 50}{space 1}   -0.74{col 59}{space 3}0.459{col 67}{space 4} .5502917{col 80}{space 3} 1.309466
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  .683341{col 39}{space 2} .1570662{col 50}{space 1}   -1.66{col 59}{space 3}0.098{col 67}{space 4} .4355002{col 80}{space 3} 1.072227
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8716696{col 39}{space 2} .1227484{col 50}{space 1}   -0.98{col 59}{space 3}0.329{col 67}{space 4} .6614329{col 80}{space 3}  1.14873
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.146718{col 39}{space 2}  .222469{col 50}{space 1}    0.71{col 59}{space 3}0.480{col 67}{space 4} .7840056{col 80}{space 3} 1.677235
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.314641{col 39}{space 2} .0823357{col 50}{space 1}    4.37{col 59}{space 3}0.000{col 67}{space 4} 1.162778{col 80}{space 3} 1.486339
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2}  .511898{col 39}{space 2} .0782173{col 50}{space 1}   -4.38{col 59}{space 3}0.000{col 67}{space 4} .3794207{col 80}{space 3} .6906307
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .8426076{col 39}{space 2} .1670319{col 50}{space 1}   -0.86{col 59}{space 3}0.388{col 67}{space 4} .5713337{col 80}{space 3} 1.242685
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9967418{col 39}{space 2} .0109982{col 50}{space 1}   -0.30{col 59}{space 3}0.767{col 67}{space 4} .9754172{col 80}{space 3} 1.018533
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9657065{col 39}{space 2} .0329502{col 50}{space 1}   -1.02{col 59}{space 3}0.306{col 67}{space 4} .9032374{col 80}{space 3} 1.032496
{txt}{space 16}lenep_new {c |}{col 27}{res}{space 2} .9695572{col 39}{space 2} .0568512{col 50}{space 1}   -0.53{col 59}{space 3}0.598{col 67}{space 4} .8642952{col 80}{space 3} 1.087639
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .8761734{col 39}{space 2}   .48178{col 50}{space 1}   -0.24{col 59}{space 3}0.810{col 67}{space 4} .2982231{col 80}{space 3} 2.574179
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .0468815{col 39}{space 2}  .066405{col 67}{space 4} .0029196{col 80}{space 3} .7527998
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}25
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea16.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A16. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea16.rtf"'})

{com}. 
. *************
. **Table A17**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined mean_min_eucl if p_niche==0 & mainniche2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-7088.5349}  
Iteration 1:{space 3}log likelihood = {res:-6723.8276}  
Iteration 2:{space 3}log likelihood = {res: -6721.994}  
Iteration 3:{space 3}log likelihood = {res:-6721.9914}  
Iteration 4:{space 3}log likelihood = {res:-6721.9914}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6588.6812}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6588.6812}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6584.4618}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6578.2375}  
Iteration 3:{space 3}log pseudolikelihood = {res:-6572.7363}  
Iteration 4:{space 3}log pseudolikelihood = {res:-6572.7145}  
Iteration 5:{space 3}log pseudolikelihood = {res:-6572.7144}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,842
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        37

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}     671.4
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   396.64
{txt}Log pseudolikelihood = {res}-6572.7144{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:37} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.070425{col 39}{space 2} .0695184{col 50}{space 1}    1.05{col 59}{space 3}0.295{col 67}{space 4} .9424866{col 80}{space 3}  1.21573
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9847123{col 39}{space 2} .0025931{col 50}{space 1}   -5.85{col 59}{space 3}0.000{col 67}{space 4}  .979643{col 80}{space 3} .9898078
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.233737{col 39}{space 2} .1170192{col 50}{space 1}    2.21{col 59}{space 3}0.027{col 67}{space 4} 1.024441{col 80}{space 3} 1.485794
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.306658{col 39}{space 2} .1609867{col 50}{space 1}    2.17{col 59}{space 3}0.030{col 67}{space 4} 1.026336{col 80}{space 3} 1.663544
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8170719{col 39}{space 2} .0570454{col 50}{space 1}   -2.89{col 59}{space 3}0.004{col 67}{space 4} .7125774{col 80}{space 3} .9368899
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6547438{col 39}{space 2} .0620904{col 50}{space 1}   -4.47{col 59}{space 3}0.000{col 67}{space 4}  .543689{col 80}{space 3} .7884827
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.109002{col 39}{space 2} .1814587{col 50}{space 1}    8.67{col 59}{space 3}0.000{col 67}{space 4} 1.781721{col 80}{space 3} 2.496402
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2}  .971701{col 39}{space 2}  .035338{col 50}{space 1}   -0.79{col 59}{space 3}0.430{col 67}{space 4} .9048506{col 80}{space 3}  1.04349
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3952583{col 39}{space 2} .0417034{col 50}{space 1}   -8.80{col 59}{space 3}0.000{col 67}{space 4} .3214188{col 80}{space 3} .4860609
{txt}{space 13}p_government {c |}{col 27}{res}{space 2}  1.24951{col 39}{space 2}  .255498{col 50}{space 1}    1.09{col 59}{space 3}0.276{col 67}{space 4} .8369267{col 80}{space 3} 1.865487
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2}  1.01621{col 39}{space 2} .0099741{col 50}{space 1}    1.64{col 59}{space 3}0.101{col 67}{space 4} .9968476{col 80}{space 3} 1.035948
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} 1.017607{col 39}{space 2} .0090286{col 50}{space 1}    1.97{col 59}{space 3}0.049{col 67}{space 4} 1.000065{col 80}{space 3} 1.035458
{txt}{space 12}mean_min_eucl {c |}{col 27}{res}{space 2}  .456504{col 39}{space 2} .2178086{col 50}{space 1}   -1.64{col 59}{space 3}0.100{col 67}{space 4} .1791912{col 80}{space 3} 1.162981
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .2012154{col 39}{space 2} .1091543{col 50}{space 1}   -2.96{col 59}{space 3}0.003{col 67}{space 4} .0694873{col 80}{space 3}  .582662
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .3872634{col 39}{space 2} .1412656{col 67}{space 4} .1894549{col 80}{space 3} .7916023
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}37
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined mean_min_eucl if p_niche==1 & nichemain2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2164.0896}  
Iteration 1:{space 3}log likelihood = {res:-2159.1522}  
Iteration 2:{space 3}log likelihood = {res:-2159.1488}  
Iteration 3:{space 3}log likelihood = {res:-2159.1488}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2143.6596}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2143.6596}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2137.8601}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2136.5376}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2136.1951}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2136.1892}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2136.1892}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,295
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        30

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     143.2
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   211.46
{txt}Log pseudolikelihood = {res}-2136.1892{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:30} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8158762{col 39}{space 2} .0906537{col 50}{space 1}   -1.83{col 59}{space 3}0.067{col 67}{space 4} .6562141{col 80}{space 3} 1.014385
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9927407{col 39}{space 2} .0029242{col 50}{space 1}   -2.47{col 59}{space 3}0.013{col 67}{space 4}  .987026{col 80}{space 3} .9984886
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.299772{col 39}{space 2} .1788369{col 50}{space 1}    1.91{col 59}{space 3}0.057{col 67}{space 4}  .992543{col 80}{space 3} 1.702099
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.075901{col 39}{space 2} .1667155{col 50}{space 1}    0.47{col 59}{space 3}0.637{col 67}{space 4} .7940991{col 80}{space 3} 1.457705
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.138477{col 39}{space 2} .1186981{col 50}{space 1}    1.24{col 59}{space 3}0.214{col 67}{space 4} .9280631{col 80}{space 3} 1.396596
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.579389{col 39}{space 2} .1976803{col 50}{space 1}    3.65{col 59}{space 3}0.000{col 67}{space 4} 1.235807{col 80}{space 3} 2.018495
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .8144665{col 39}{space 2} .0711971{col 50}{space 1}   -2.35{col 59}{space 3}0.019{col 67}{space 4} .6862224{col 80}{space 3} .9666773
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.106048{col 39}{space 2} .0410046{col 50}{space 1}    2.72{col 59}{space 3}0.007{col 67}{space 4}  1.02853{col 80}{space 3} 1.189407
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4291727{col 39}{space 2} .0425183{col 50}{space 1}   -8.54{col 59}{space 3}0.000{col 67}{space 4} .3534299{col 80}{space 3} .5211477
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7495589{col 39}{space 2} .1466977{col 50}{space 1}   -1.47{col 59}{space 3}0.141{col 67}{space 4} .5107582{col 80}{space 3} 1.100009
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2}  .994898{col 39}{space 2} .0128364{col 50}{space 1}   -0.40{col 59}{space 3}0.692{col 67}{space 4} .9700545{col 80}{space 3} 1.020378
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9923524{col 39}{space 2} .0074141{col 50}{space 1}   -1.03{col 59}{space 3}0.304{col 67}{space 4} .9779269{col 80}{space 3} 1.006991
{txt}{space 12}mean_min_eucl {c |}{col 27}{res}{space 2} 1.215696{col 39}{space 2} .3143174{col 50}{space 1}    0.76{col 59}{space 3}0.450{col 67}{space 4} .7323982{col 80}{space 3} 2.017913
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}  .521401{col 39}{space 2} .2417014{col 50}{space 1}   -1.40{col 59}{space 3}0.160{col 67}{space 4} .2101782{col 80}{space 3} 1.293469
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .2271981{col 39}{space 2} .1636492{col 67}{space 4}  .055372{col 80}{space 3} .9322216
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}30
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined mean_min_eucl if p_radicalrl_vs_mainstream==0 & mainradical2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -5267.485}  
Iteration 1:{space 3}log likelihood = {res:-4615.1931}  
Iteration 2:{space 3}log likelihood = {res:-4605.6059}  
Iteration 3:{space 3}log likelihood = {res:-4605.5688}  
Iteration 4:{space 3}log likelihood = {res:-4605.5688}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4317.5835}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4317.5835}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4299.4948}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4296.8225}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4296.6246}  
Iteration 4:{space 3}log pseudolikelihood = {res:-4296.6227}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4296.6227}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,037
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        37

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}     649.6
{col 63}{txt}max{col 67}={res}{col 69}     1,269

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}  1050.03
{txt}Log pseudolikelihood = {res}-4296.6227{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:37} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.292874{col 40}{space 2} .0907952{col 51}{space 1}    3.66{col 60}{space 3}0.000{col 68}{space 4} 1.126623{col 81}{space 3} 1.483658
{txt}{space 23}age {c |}{col 28}{res}{space 2}    .9877{col 40}{space 2} .0030646{col 51}{space 1}   -3.99{col 60}{space 3}0.000{col 68}{space 4} .9817117{col 81}{space 3} .9937248
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.326409{col 40}{space 2} .1876115{col 51}{space 1}    2.00{col 60}{space 3}0.046{col 68}{space 4} 1.005266{col 81}{space 3} 1.750145
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.095824{col 40}{space 2}  .179528{col 51}{space 1}    0.56{col 60}{space 3}0.576{col 68}{space 4} .7948575{col 81}{space 3}  1.51075
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.717484{col 40}{space 2} .2661186{col 51}{space 1}   10.21{col 60}{space 3}0.000{col 68}{space 4} 2.242902{col 81}{space 3} 3.292484
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7443408{col 40}{space 2}   .06148{col 51}{space 1}   -3.57{col 60}{space 3}0.000{col 68}{space 4}   .63309{col 81}{space 3} .8751413
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5495755{col 40}{space 2} .0535165{col 51}{space 1}   -6.15{col 60}{space 3}0.000{col 68}{space 4} .4540871{col 81}{space 3} .6651438
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.036714{col 40}{space 2} .0434404{col 51}{space 1}    0.86{col 60}{space 3}0.390{col 68}{space 4} .9549749{col 81}{space 3} 1.125449
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3526521{col 40}{space 2} .0470346{col 51}{space 1}   -7.81{col 60}{space 3}0.000{col 68}{space 4} .2715304{col 81}{space 3} .4580096
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.178457{col 40}{space 2} .2782099{col 51}{space 1}    0.70{col 60}{space 3}0.487{col 68}{space 4} .7419286{col 81}{space 3} 1.871825
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9557222{col 40}{space 2} .0351401{col 51}{space 1}   -1.23{col 60}{space 3}0.218{col 68}{space 4} .8892719{col 81}{space 3} 1.027138
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 1.096938{col 40}{space 2} .0359558{col 51}{space 1}    2.82{col 60}{space 3}0.005{col 68}{space 4} 1.028682{col 81}{space 3} 1.169723
{txt}{space 13}mean_min_eucl {c |}{col 28}{res}{space 2} .2320419{col 40}{space 2} .2115623{col 51}{space 1}   -1.60{col 60}{space 3}0.109{col 68}{space 4} .0388589{col 81}{space 3} 1.385615
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .1412252{col 40}{space 2} .1350493{col 51}{space 1}   -2.05{col 60}{space 3}0.041{col 68}{space 4} .0216738{col 81}{space 3} .9202162
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 2.199546{col 40}{space 2} .8740624{col 68}{space 4} 1.009439{col 81}{space 3} 4.792766
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}37
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined mean_min_eucl if p_radicalrl_vs_mainstream==1 & radicalmain2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1282.6027}  
Iteration 1:{space 3}log likelihood = {res:  -1278.06}  
Iteration 2:{space 3}log likelihood = {res:  -1278.05}  
Iteration 3:{space 3}log likelihood = {res:  -1278.05}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1254.6196}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1254.6196}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1252.5201}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1249.8108}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1249.7404}  
Iteration 4:{space 3}log pseudolikelihood = {res:  -1249.74}  
Iteration 5:{space 3}log pseudolikelihood = {res:  -1249.74}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,610
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        25

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     104.4
{col 63}{txt}max{col 67}={res}{col 69}       296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   633.65
{txt}Log pseudolikelihood = {res}-1249.74{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:25} clusters in {res:country_elec})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8859297{col 40}{space 2} .1354307{col 51}{space 1}   -0.79{col 60}{space 3}0.428{col 68}{space 4} .6565645{col 81}{space 3} 1.195422
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9923913{col 40}{space 2} .0038897{col 51}{space 1}   -1.95{col 60}{space 3}0.051{col 68}{space 4} .9847969{col 81}{space 3} 1.000044
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.147765{col 40}{space 2} .2064805{col 51}{space 1}    0.77{col 60}{space 3}0.444{col 68}{space 4} .8067217{col 81}{space 3} 1.632986
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7524704{col 40}{space 2} .1583174{col 51}{space 1}   -1.35{col 60}{space 3}0.176{col 68}{space 4} .4981948{col 81}{space 3} 1.136527
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .6660786{col 40}{space 2} .0543386{col 51}{space 1}   -4.98{col 60}{space 3}0.000{col 68}{space 4} .5676552{col 81}{space 3} .7815672
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.113275{col 40}{space 2} .1882841{col 51}{space 1}    0.63{col 60}{space 3}0.526{col 68}{space 4} .7991746{col 81}{space 3} 1.550826
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.784902{col 40}{space 2} .2910294{col 51}{space 1}    3.55{col 60}{space 3}0.000{col 68}{space 4} 1.296658{col 81}{space 3}  2.45699
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.061259{col 40}{space 2} .0541715{col 51}{space 1}    1.16{col 60}{space 3}0.244{col 68}{space 4} .9602228{col 81}{space 3} 1.172926
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3923058{col 40}{space 2} .0434503{col 51}{space 1}   -8.45{col 60}{space 3}0.000{col 68}{space 4}  .315754{col 81}{space 3} .4874168
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.315878{col 40}{space 2} .4517936{col 51}{space 1}    0.80{col 60}{space 3}0.424{col 68}{space 4} .6713736{col 81}{space 3} 2.579094
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9792281{col 40}{space 2} .0220134{col 51}{space 1}   -0.93{col 60}{space 3}0.350{col 68}{space 4} .9370193{col 81}{space 3} 1.023338
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9784961{col 40}{space 2} .0173472{col 51}{space 1}   -1.23{col 60}{space 3}0.220{col 68}{space 4} .9450801{col 81}{space 3} 1.013094
{txt}{space 13}mean_min_eucl {c |}{col 28}{res}{space 2} .7927137{col 40}{space 2} .3050658{col 51}{space 1}   -0.60{col 60}{space 3}0.546{col 68}{space 4} .3728569{col 81}{space 3} 1.685351
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 1.211136{col 40}{space 2} .6959607{col 51}{space 1}    0.33{col 60}{space 3}0.739{col 68}{space 4} .3927019{col 81}{space 3} 3.735277
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec              {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2}  .461457{col 40}{space 2} .1871361{col 68}{space 4} .2084207{col 81}{space 3} 1.021696
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}25
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined mean_min_eucl if p_green_vs_mainstream==0 & maingreen2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3406.7884}  
Iteration 1:{space 3}log likelihood = {res:-2312.4261}  
Iteration 2:{space 3}log likelihood = {res:-2232.8085}  
Iteration 3:{space 3}log likelihood = {res:-2225.2248}  
Iteration 4:{space 3}log likelihood = {res:-2225.1393}  
Iteration 5:{space 3}log likelihood = {res:-2225.1391}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2175.4163}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2175.4163}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2168.3665}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2164.7197}  
Iteration 3:{space 3}log pseudolikelihood = {res: -2163.892}  
Iteration 4:{space 3}log pseudolikelihood = {res:  -2163.83}  
Iteration 5:{space 3}log pseudolikelihood = {res:  -2163.83}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    23,280
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        37

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        70
{col 63}{txt}avg{col 67}={res}{col 69}     629.2
{col 63}{txt}max{col 67}={res}{col 69}     1,256

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   397.01
{txt}Log pseudolikelihood = {res}-2163.83{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:37} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .6709165{col 39}{space 2} .0501574{col 50}{space 1}   -5.34{col 59}{space 3}0.000{col 67}{space 4} .5794728{col 80}{space 3} .7767905
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9790171{col 39}{space 2} .0038352{col 50}{space 1}   -5.41{col 59}{space 3}0.000{col 67}{space 4}  .971529{col 80}{space 3} .9865629
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.813982{col 39}{space 2} .6365483{col 50}{space 1}    1.70{col 59}{space 3}0.090{col 67}{space 4} .9118768{col 80}{space 3} 3.608527
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 3.382146{col 39}{space 2} 1.437604{col 50}{space 1}    2.87{col 59}{space 3}0.004{col 67}{space 4} 1.470224{col 80}{space 3}  7.78039
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.139691{col 39}{space 2} .1604203{col 50}{space 1}    0.93{col 59}{space 3}0.353{col 67}{space 4} .8649165{col 80}{space 3}  1.50176
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.041699{col 39}{space 2} .1437222{col 50}{space 1}    0.30{col 59}{space 3}0.767{col 67}{space 4} .7948822{col 80}{space 3} 1.365154
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9838614{col 39}{space 2}  .197775{col 50}{space 1}   -0.08{col 59}{space 3}0.935{col 67}{space 4} .6634773{col 80}{space 3} 1.458954
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9153053{col 39}{space 2} .0442394{col 50}{space 1}   -1.83{col 59}{space 3}0.067{col 67}{space 4} .8325779{col 80}{space 3} 1.006253
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5285818{col 39}{space 2} .0561519{col 50}{space 1}   -6.00{col 59}{space 3}0.000{col 67}{space 4} .4292279{col 80}{space 3} .6509332
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.604264{col 39}{space 2} .4705024{col 50}{space 1}    1.61{col 59}{space 3}0.107{col 67}{space 4} .9028867{col 80}{space 3} 2.850482
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9898421{col 39}{space 2} .0317187{col 50}{space 1}   -0.32{col 59}{space 3}0.750{col 67}{space 4} .9295866{col 80}{space 3} 1.054003
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.807058{col 39}{space 2} .1977382{col 50}{space 1}    5.41{col 59}{space 3}0.000{col 67}{space 4}  1.45824{col 80}{space 3} 2.239316
{txt}{space 12}mean_min_eucl {c |}{col 27}{res}{space 2} 2.425778{col 39}{space 2} 1.715233{col 50}{space 1}    1.25{col 59}{space 3}0.210{col 67}{space 4} .6067063{col 80}{space 3} 9.698926
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0004224{col 39}{space 2} .0005611{col 50}{space 1}   -5.85{col 59}{space 3}0.000{col 67}{space 4} .0000313{col 80}{space 3} .0057061
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.609867{col 39}{space 2} .6578311{col 67}{space 4} .7227169{col 80}{space 3} 3.586012
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}37
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined mean_min_eucl if p_green_vs_mainstream==1 & greenmain2==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-571.43457}  
Iteration 1:{space 3}log likelihood = {res:-570.77008}  
Iteration 2:{space 3}log likelihood = {res:-570.76987}  
Iteration 3:{space 3}log likelihood = {res:-570.76987}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-580.71138}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-580.71138}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-570.99355}  
Iteration 2:{space 3}log pseudolikelihood = {res:-570.77037}  
Iteration 3:{space 3}log pseudolikelihood = {res: -570.7587}  
Iteration 4:{space 3}log pseudolikelihood = {res:-570.75866}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,003
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        20

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}      50.1
{col 63}{txt}max{col 67}={res}{col 69}       177

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   235.99
{txt}Log pseudolikelihood = {res}-570.75866{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:20} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .7784123{col 39}{space 2}  .099503{col 50}{space 1}   -1.96{col 59}{space 3}0.050{col 67}{space 4} .6059017{col 80}{space 3}  1.00004
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9981997{col 39}{space 2} .0065563{col 50}{space 1}   -0.27{col 59}{space 3}0.784{col 67}{space 4}  .985432{col 80}{space 3} 1.011133
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.144044{col 39}{space 2} .3420464{col 50}{space 1}    0.45{col 59}{space 3}0.653{col 67}{space 4} .6367223{col 80}{space 3} 2.055583
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.068742{col 39}{space 2} .3692617{col 50}{space 1}    0.19{col 59}{space 3}0.847{col 67}{space 4}  .542967{col 80}{space 3} 2.103643
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.313223{col 39}{space 2} .1604009{col 50}{space 1}    2.23{col 59}{space 3}0.026{col 67}{space 4} 1.033642{col 80}{space 3} 1.668425
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9490273{col 39}{space 2} .1365337{col 50}{space 1}   -0.36{col 59}{space 3}0.716{col 67}{space 4} .7158448{col 80}{space 3} 1.258168
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9915541{col 39}{space 2} .1964946{col 50}{space 1}   -0.04{col 59}{space 3}0.966{col 67}{space 4} .6724117{col 80}{space 3} 1.462169
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.218098{col 39}{space 2} .0891619{col 50}{space 1}    2.70{col 59}{space 3}0.007{col 67}{space 4} 1.055301{col 80}{space 3} 1.406009
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5493992{col 39}{space 2} .0991538{col 50}{space 1}   -3.32{col 59}{space 3}0.001{col 67}{space 4} .3857145{col 80}{space 3} .7825464
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .8371194{col 39}{space 2} .2826349{col 50}{space 1}   -0.53{col 59}{space 3}0.598{col 67}{space 4} .4319147{col 80}{space 3}  1.62247
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.001663{col 39}{space 2} .0116992{col 50}{space 1}    0.14{col 59}{space 3}0.887{col 67}{space 4} .9789938{col 80}{space 3} 1.024858
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9405199{col 39}{space 2} .0222948{col 50}{space 1}   -2.59{col 59}{space 3}0.010{col 67}{space 4} .8978225{col 80}{space 3} .9852478
{txt}{space 12}mean_min_eucl {c |}{col 27}{res}{space 2} 1.498181{col 39}{space 2} .4617473{col 50}{space 1}    1.31{col 59}{space 3}0.190{col 67}{space 4} .8188785{col 80}{space 3}    2.741
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .4609407{col 39}{space 2} .2566547{col 50}{space 1}   -1.39{col 59}{space 3}0.164{col 67}{space 4} .1547714{col 80}{space 3} 1.372776
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .0058485{col 39}{space 2}  .051977{col 67}{space 4} 1.59e-10{col 80}{space 3} 214733.9
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}20
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea17.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A17. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea17.rtf"'})

{com}. 
. *************
. **Table A18**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_niche==0 ///
> || ccode: || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -7330.739}  
Iteration 1:{space 3}log likelihood = {res:-6961.3936}  
Iteration 2:{space 3}log likelihood = {res:-6959.7544}  
Iteration 3:{space 3}log likelihood = {res:-6959.7524}  
Iteration 4:{space 3}log likelihood = {res:-6959.7524}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6805.4718}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6805.4718}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6800.2063}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-6797.7848}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-6795.6049}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-6795.4954}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-6795.4516}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res: -6795.434}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:  -6795.42}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-6795.3975}  
Iteration 9:{space 3}log pseudolikelihood = {res:-6795.3896}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res:-6795.3757}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res:-6795.3631}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res:-6795.3507}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res:-6795.3385}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res:-6795.3264}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res:-6795.3144}  (backed up)
Iteration 16:{space 2}log pseudolikelihood = {res:-6795.3026}  (backed up)
Iteration 17:{space 2}log pseudolikelihood = {res:-6795.2909}  (backed up)
Iteration 18:{space 2}log pseudolikelihood = {res:-6795.2793}  (backed up)
Iteration 19:{space 2}log pseudolikelihood = {res:-6795.2678}  (backed up)
Iteration 20:{space 2}log pseudolikelihood = {res:-6795.2564}  (backed up)
Iteration 21:{space 2}log pseudolikelihood = {res:-6795.2451}  (backed up)
Iteration 22:{space 2}log pseudolikelihood = {res:-6795.2339}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-6795.2228}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-6795.2118}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-6795.2008}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:  -6795.19}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-6795.1792}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-6795.1685}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-6795.1579}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-6795.1474}  (backed up)
Iteration 31:{space 2}log pseudolikelihood = {res:-6795.1369}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-6795.1265}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-6795.1162}  (backed up)
Iteration 34:{space 2}log pseudolikelihood = {res:-6795.1059}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-6795.0957}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-6795.0856}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res:-6795.0755}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-6795.0654}  (backed up)
Iteration 39:{space 2}log pseudolikelihood = {res:-6795.0555}  (backed up)
Iteration 40:{space 2}log pseudolikelihood = {res:-6795.0455}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res:-6795.0357}  (backed up)
Iteration 42:{space 2}log pseudolikelihood = {res:-6795.0259}  (backed up)
Iteration 43:{space 2}log pseudolikelihood = {res:-6795.0161}  (backed up)
Iteration 44:{space 2}log pseudolikelihood = {res:-6795.0064}  (backed up)
Iteration 45:{space 2}log pseudolikelihood = {res:-6794.9967}  (backed up)
Iteration 46:{space 2}log pseudolikelihood = {res:-6794.9775}  (backed up)
Iteration 47:{space 2}log pseudolikelihood = {res:-6794.9727}  (not concave)
Iteration 48:{space 2}log pseudolikelihood = {res:-6794.9722}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-6794.9711}  
Iteration 50:{space 2}log pseudolikelihood = {res:-6794.9331}  (not concave)
Iteration 51:{space 2}log pseudolikelihood = {res:-6794.9179}  
Iteration 52:{space 2}log pseudolikelihood = {res:-6794.9087}  (not concave)
Iteration 53:{space 2}log pseudolikelihood = {res: -6794.879}  
Iteration 54:{space 2}log pseudolikelihood = {res:-6794.5881}  (not concave)
Iteration 55:{space 2}log pseudolikelihood = {res: -6794.356}  
Iteration 56:{space 2}log pseudolikelihood = {res:-6793.8715}  (not concave)
Iteration 57:{space 2}log pseudolikelihood = {res:-6793.6782}  
Iteration 58:{space 2}log pseudolikelihood = {res:-6787.5071}  
Iteration 59:{space 2}log pseudolikelihood = {res:-6786.5994}  
Iteration 60:{space 2}log pseudolikelihood = {res:-6773.2517}  (not concave)
Iteration 61:{space 2}log pseudolikelihood = {res:-6773.2517}  (not concave)
Iteration 62:{space 2}log pseudolikelihood = {res:-6773.2506}  
Iteration 63:{space 2}log pseudolikelihood = {res:-6773.1457}  
Iteration 64:{space 2}log pseudolikelihood = {res:-6773.1451}  (not concave)
Iteration 65:{space 2}log pseudolikelihood = {res:-6773.1451}  (not concave)
Iteration 66:{space 2}log pseudolikelihood = {res:-6773.1451}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,872

{txt}{col 9}Grouping information
{col 9}{txt}{hline 16}{c TT}{hline 44}
{col 25}{txt}{c |}{col 31}No. of{col 44}Observations per group
{col 10}{txt}Group variable{col 25}{c |}{col 31}groups{col 41}Minimum{col 52}Average{col 63}Maximum
{col 9}{txt}{hline 16}{c +}{hline 44}
{col 19}{res}ccode{col 25}{txt}{c |}{res}{col 29}      15{col 39}       80{col 50}  1,724.8{col 61}    5,125
{col 12}{res}country_elec{col 25}{txt}{c |}{res}{col 29}      39{col 39}       80{col 50}    663.4{col 61}    1,296
{col 9}{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}202122.50
{txt}Log pseudolikelihood = {res}-6773.1451{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:15} clusters in {res:ccode})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.085267{col 39}{space 2} .0627334{col 50}{space 1}    1.42{col 59}{space 3}0.157{col 67}{space 4} .9690211{col 80}{space 3} 1.215458
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9845907{col 39}{space 2} .0025338{col 50}{space 1}   -6.03{col 59}{space 3}0.000{col 67}{space 4}  .979637{col 80}{space 3} .9895695
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.273161{col 39}{space 2} .1142114{col 50}{space 1}    2.69{col 59}{space 3}0.007{col 67}{space 4} 1.067885{col 80}{space 3} 1.517896
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.377759{col 39}{space 2} .1763211{col 50}{space 1}    2.50{col 59}{space 3}0.012{col 67}{space 4}  1.07211{col 80}{space 3} 1.770546
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8141882{col 39}{space 2} .0603009{col 50}{space 1}   -2.78{col 59}{space 3}0.006{col 67}{space 4} .7041782{col 80}{space 3} .9413844
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6456304{col 39}{space 2} .0607992{col 50}{space 1}   -4.65{col 59}{space 3}0.000{col 67}{space 4} .5368168{col 80}{space 3} .7765007
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.099625{col 39}{space 2}  .232329{col 50}{space 1}    6.70{col 59}{space 3}0.000{col 67}{space 4} 1.690262{col 80}{space 3} 2.608131
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9736146{col 39}{space 2} .0373366{col 50}{space 1}   -0.70{col 59}{space 3}0.486{col 67}{space 4} .9031187{col 80}{space 3} 1.049613
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3945243{col 39}{space 2} .0634185{col 50}{space 1}   -5.79{col 59}{space 3}0.000{col 67}{space 4} .2879028{col 80}{space 3} .5406318
{txt}{space 13}p_government {c |}{col 27}{res}{space 2}  1.21842{col 39}{space 2} .1973132{col 50}{space 1}    1.22{col 59}{space 3}0.222{col 67}{space 4} .8870582{col 80}{space 3} 1.673564
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.016465{col 39}{space 2}  .009533{col 50}{space 1}    1.74{col 59}{space 3}0.082{col 67}{space 4} .9979516{col 80}{space 3} 1.035322
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2}  1.02665{col 39}{space 2} .0137926{col 50}{space 1}    1.96{col 59}{space 3}0.050{col 67}{space 4} .9999699{col 80}{space 3} 1.054042
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.140852{col 39}{space 2} .1070404{col 50}{space 1}    1.40{col 59}{space 3}0.160{col 67}{space 4} .9492171{col 80}{space 3} 1.371177
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0784221{col 39}{space 2} .0228765{col 50}{space 1}   -8.73{col 59}{space 3}0.000{col 67}{space 4} .0442725{col 80}{space 3} .1389127
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                    {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 6.76e-32{col 39}{space 2} 2.33e-31{col 67}{space 4} 7.76e-35{col 80}{space 3} 5.88e-29
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode>country_elec       {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4328165{col 39}{space 2} .2204475{col 67}{space 4} .1594995{col 80}{space 3} 1.174487
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_countries = e(N_clust)

{txt}added scalar:
        e(N_countries) =  {res}15
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_niche==1 ///
> || ccode: || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2261.6559}  
Iteration 1:{space 3}log likelihood = {res:-2256.0369}  
Iteration 2:{space 3}log likelihood = {res:-2256.0321}  
Iteration 3:{space 3}log likelihood = {res:-2256.0321}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2249.7254}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2249.7254}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2242.8123}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-2239.4141}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-2237.9617}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-2237.3821}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-2236.9329}  
Iteration 6:{space 3}log pseudolikelihood = {res:-2236.9313}  (backed up)
Iteration 7:{space 3}log pseudolikelihood = {res:-2236.9301}  (backed up)
Iteration 8:{space 3}log pseudolikelihood = {res:-2236.9287}  (backed up)
Iteration 9:{space 3}log pseudolikelihood = {res:-2236.9277}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res:-2236.9266}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res:-2236.9256}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res:-2236.9245}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res:-2236.9235}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res:-2236.9225}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res:-2236.9215}  (backed up)
Iteration 16:{space 2}log pseudolikelihood = {res:-2236.9204}  (backed up)
Iteration 17:{space 2}log pseudolikelihood = {res:-2236.9194}  (backed up)
Iteration 18:{space 2}log pseudolikelihood = {res:-2236.9184}  (backed up)
Iteration 19:{space 2}log pseudolikelihood = {res:-2236.9174}  (backed up)
Iteration 20:{space 2}log pseudolikelihood = {res:-2236.9163}  (backed up)
Iteration 21:{space 2}log pseudolikelihood = {res:-2236.9153}  (backed up)
Iteration 22:{space 2}log pseudolikelihood = {res:-2236.9143}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-2236.9133}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-2236.9123}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-2236.9113}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:-2236.9102}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-2236.9092}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-2236.9082}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-2236.9072}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-2236.9062}  (backed up)
Iteration 31:{space 2}log pseudolikelihood = {res:-2236.9052}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-2236.9042}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-2236.9032}  (backed up)
Iteration 34:{space 2}log pseudolikelihood = {res:-2236.9022}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-2236.9012}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-2236.9001}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res:-2236.8991}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-2236.8981}  (backed up)
Iteration 39:{space 2}log pseudolikelihood = {res:-2236.8971}  (backed up)
Iteration 40:{space 2}log pseudolikelihood = {res:-2236.8961}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res:-2236.8951}  (backed up)
Iteration 42:{space 2}log pseudolikelihood = {res:-2236.8941}  (backed up)
Iteration 43:{space 2}log pseudolikelihood = {res:-2236.8931}  (backed up)
Iteration 44:{space 2}log pseudolikelihood = {res:-2236.8921}  (backed up)
Iteration 45:{space 2}log pseudolikelihood = {res:-2236.8911}  (backed up)
Iteration 46:{space 2}log pseudolikelihood = {res:-2236.8906}  (backed up)
Iteration 47:{space 2}log pseudolikelihood = {res:-2236.8896}  (backed up)
Iteration 48:{space 2}log pseudolikelihood = {res:-2236.8895}  (backed up)
Iteration 49:{space 2}log pseudolikelihood = {res: -2236.889}  (backed up)
Iteration 50:{space 2}log pseudolikelihood = {res: -2236.888}  (not concave)
Iteration 51:{space 2}log pseudolikelihood = {res: -2236.888}  (not concave)
Iteration 52:{space 2}log pseudolikelihood = {res:-2236.8873}  
Iteration 53:{space 2}log pseudolikelihood = {res:-2236.8794}  (not concave)
Iteration 54:{space 2}log pseudolikelihood = {res:-2236.8786}  (not concave)
Iteration 55:{space 2}log pseudolikelihood = {res: -2236.876}  (not concave)
Iteration 56:{space 2}log pseudolikelihood = {res:-2236.8679}  
Iteration 57:{space 2}log pseudolikelihood = {res:-2236.6267}  
Iteration 58:{space 2}log pseudolikelihood = {res:-2235.5692}  (not concave)
Iteration 59:{space 2}log pseudolikelihood = {res:-2235.5626}  (not concave)
Iteration 60:{space 2}log pseudolikelihood = {res:-2235.5574}  (not concave)
Iteration 61:{space 2}log pseudolikelihood = {res:-2235.5533}  (not concave)
Iteration 62:{space 2}log pseudolikelihood = {res:-2235.5467}  (not concave)
Iteration 63:{space 2}log pseudolikelihood = {res: -2235.381}  
Iteration 64:{space 2}log pseudolikelihood = {res:-2235.3711}  (not concave)
Iteration 65:{space 2}log pseudolikelihood = {res:-2235.3711}  (not concave)
Iteration 66:{space 2}log pseudolikelihood = {res:-2235.3711}  (not concave)
Iteration 67:{space 2}log pseudolikelihood = {res:-2235.3711}  (not concave)
Iteration 68:{space 2}log pseudolikelihood = {res:-2235.3711}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,515

{txt}{col 9}Grouping information
{col 9}{txt}{hline 16}{c TT}{hline 44}
{col 25}{txt}{c |}{col 31}No. of{col 44}Observations per group
{col 10}{txt}Group variable{col 25}{c |}{col 31}groups{col 41}Minimum{col 52}Average{col 63}Maximum
{col 9}{txt}{hline 16}{c +}{hline 44}
{col 19}{res}ccode{col 25}{txt}{c |}{res}{col 29}      15{col 39}        8{col 50}    301.0{col 61}      987
{col 12}{res}country_elec{col 25}{txt}{c |}{res}{col 29}      32{col 39}        8{col 50}    141.1{col 61}      420
{col 9}{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   960.98
{txt}Log pseudolikelihood = {res}-2235.3711{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:15} clusters in {res:ccode})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8236383{col 39}{space 2} .0951091{col 50}{space 1}   -1.68{col 59}{space 3}0.093{col 67}{space 4} .6568173{col 80}{space 3} 1.032829
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9932298{col 39}{space 2} .0028157{col 50}{space 1}   -2.40{col 59}{space 3}0.017{col 67}{space 4} .9877264{col 80}{space 3} .9987639
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.298021{col 39}{space 2}  .211937{col 50}{space 1}    1.60{col 59}{space 3}0.110{col 67}{space 4} .9425403{col 80}{space 3} 1.787571
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.039557{col 39}{space 2}   .15654{col 50}{space 1}    0.26{col 59}{space 3}0.797{col 67}{space 4} .7738763{col 80}{space 3} 1.396449
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.125468{col 39}{space 2} .0898779{col 50}{space 1}    1.48{col 59}{space 3}0.139{col 67}{space 4} .9624051{col 80}{space 3}  1.31616
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.612879{col 39}{space 2} .2006573{col 50}{space 1}    3.84{col 59}{space 3}0.000{col 67}{space 4} 1.263875{col 80}{space 3} 2.058255
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .7953365{col 39}{space 2} .0665493{col 50}{space 1}   -2.74{col 59}{space 3}0.006{col 67}{space 4} .6750364{col 80}{space 3} .9370757
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.118206{col 39}{space 2} .0381827{col 50}{space 1}    3.27{col 59}{space 3}0.001{col 67}{space 4} 1.045819{col 80}{space 3} 1.195604
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2}   .42797{col 39}{space 2} .0444512{col 50}{space 1}   -8.17{col 59}{space 3}0.000{col 67}{space 4} .3491428{col 80}{space 3} .5245944
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7525354{col 39}{space 2} .0953007{col 50}{space 1}   -2.25{col 59}{space 3}0.025{col 67}{space 4} .5871259{col 80}{space 3} .9645453
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2}  .991582{col 39}{space 2} .0106245{col 50}{space 1}   -0.79{col 59}{space 3}0.430{col 67}{space 4} .9709755{col 80}{space 3} 1.012626
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9944723{col 39}{space 2}  .006975{col 50}{space 1}   -0.79{col 59}{space 3}0.429{col 67}{space 4}  .980895{col 80}{space 3} 1.008238
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .9128909{col 39}{space 2}  .045697{col 50}{space 1}   -1.82{col 59}{space 3}0.069{col 67}{space 4} .8275799{col 80}{space 3} 1.006996
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .5941466{col 39}{space 2} .2301799{col 50}{space 1}   -1.34{col 59}{space 3}0.179{col 67}{space 4} .2780528{col 80}{space 3}  1.26958
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                    {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 4.36e-33{col 39}{space 2} 2.67e-32{col 67}{space 4} 2.68e-38{col 80}{space 3} 7.10e-28
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode>country_elec       {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1917975{col 39}{space 2} .1579728{col 67}{space 4}  .038173{col 80}{space 3} .9636732
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_countries = e(N_clust)

{txt}added scalar:
        e(N_countries) =  {res}15
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> || ccode: || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5446.0864}  
Iteration 1:{space 3}log likelihood = {res:-4784.8472}  
Iteration 2:{space 3}log likelihood = {res:-4772.3847}  
Iteration 3:{space 3}log likelihood = {res: -4772.316}  
Iteration 4:{space 3}log likelihood = {res: -4772.316}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4450.5219}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4450.5219}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-4440.2897}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4429.9661}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4418.2607}  
Iteration 4:{space 3}log pseudolikelihood = {res:-4417.8204}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4417.8104}  
Iteration 6:{space 3}log pseudolikelihood = {res:-4417.8104}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,042

{txt}{col 9}Grouping information
{col 9}{txt}{hline 16}{c TT}{hline 44}
{col 25}{txt}{c |}{col 31}No. of{col 44}Observations per group
{col 10}{txt}Group variable{col 25}{c |}{col 31}groups{col 41}Minimum{col 52}Average{col 63}Maximum
{col 9}{txt}{hline 16}{c +}{hline 44}
{col 19}{res}ccode{col 25}{txt}{c |}{res}{col 29}      15{col 39}       78{col 50}  1,669.5{col 61}    4,920
{col 12}{res}country_elec{col 25}{txt}{c |}{res}{col 29}      39{col 39}       78{col 50}    642.1{col 61}    1,269
{col 9}{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70} 47215.67
{txt}Log pseudolikelihood = {res}-4417.8104{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 92:(Std. err. adjusted for {res:15} clusters in {res:ccode})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.314868{col 40}{space 2} .0946475{col 51}{space 1}    3.80{col 60}{space 3}0.000{col 68}{space 4} 1.141854{col 81}{space 3} 1.514097
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9876133{col 40}{space 2} .0030448{col 51}{space 1}   -4.04{col 60}{space 3}0.000{col 68}{space 4} .9816636{col 81}{space 3} .9935992
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.372947{col 40}{space 2} .1867456{col 51}{space 1}    2.33{col 60}{space 3}0.020{col 68}{space 4} 1.051659{col 81}{space 3}  1.79239
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.159202{col 40}{space 2} .2276943{col 51}{space 1}    0.75{col 60}{space 3}0.452{col 68}{space 4} .7887937{col 81}{space 3}  1.70355
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.705425{col 40}{space 2} .3514533{col 51}{space 1}    7.66{col 60}{space 3}0.000{col 68}{space 4} 2.097291{col 81}{space 3} 3.489896
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7410202{col 40}{space 2} .0664344{col 51}{space 1}   -3.34{col 60}{space 3}0.001{col 68}{space 4} .6216095{col 81}{space 3} .8833696
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5435185{col 40}{space 2} .0553291{col 51}{space 1}   -5.99{col 60}{space 3}0.000{col 68}{space 4} .4452087{col 81}{space 3} .6635367
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.037341{col 40}{space 2} .0462424{col 51}{space 1}    0.82{col 60}{space 3}0.411{col 68}{space 4} .9505542{col 81}{space 3} 1.132052
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3516328{col 40}{space 2} .0652147{col 51}{space 1}   -5.64{col 60}{space 3}0.000{col 68}{space 4} .2444688{col 81}{space 3} .5057727
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.160495{col 40}{space 2} .2038519{col 51}{space 1}    0.85{col 60}{space 3}0.397{col 68}{space 4}  .822473{col 81}{space 3} 1.637438
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9637575{col 40}{space 2}  .038457{col 51}{space 1}   -0.93{col 60}{space 3}0.355{col 68}{space 4} .8912552{col 81}{space 3} 1.042158
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 1.087368{col 40}{space 2}  .062579{col 51}{space 1}    1.46{col 60}{space 3}0.146{col 68}{space 4} .9713799{col 81}{space 3} 1.217206
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} 1.876911{col 40}{space 2}  .594657{col 51}{space 1}    1.99{col 60}{space 3}0.047{col 68}{space 4} 1.008695{col 81}{space 3}  3.49243
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0333493{col 40}{space 2} .0141051{col 51}{space 1}   -8.04{col 60}{space 3}0.000{col 68}{space 4}  .014557{col 81}{space 3} .0764014
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                     {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .0446333{col 40}{space 2} .1499808{col 68}{space 4} .0000616{col 81}{space 3} 32.35494
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode>country_elec        {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 1.967835{col 40}{space 2} 1.700713{col 68}{space 4} .3616869{col 81}{space 3} 10.70643
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_countries = e(N_clust)

{txt}added scalar:
        e(N_countries) =  {res}15
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==1 ///
> || ccode: || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1323.8895}  
Iteration 1:{space 3}log likelihood = {res:-1318.8334}  
Iteration 2:{space 3}log likelihood = {res:-1318.8199}  
Iteration 3:{space 3}log likelihood = {res:-1318.8199}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -1296.865}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -1296.865}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res: -1293.711}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-1289.9069}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1288.9972}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1288.6635}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1288.6266}  (backed up)
Iteration 6:{space 3}log pseudolikelihood = {res:-1288.6178}  (backed up)
Iteration 7:{space 3}log pseudolikelihood = {res:-1288.6135}  (backed up)
Iteration 8:{space 3}log pseudolikelihood = {res:-1288.6114}  (backed up)
Iteration 9:{space 3}log pseudolikelihood = {res:-1288.6094}  (backed up)
Iteration 10:{space 2}log pseudolikelihood = {res:-1288.6073}  (backed up)
Iteration 11:{space 2}log pseudolikelihood = {res:-1288.6052}  (backed up)
Iteration 12:{space 2}log pseudolikelihood = {res:-1288.6031}  (backed up)
Iteration 13:{space 2}log pseudolikelihood = {res: -1288.601}  (backed up)
Iteration 14:{space 2}log pseudolikelihood = {res: -1288.599}  (backed up)
Iteration 15:{space 2}log pseudolikelihood = {res:-1288.5969}  (backed up)
Iteration 16:{space 2}log pseudolikelihood = {res:-1288.5948}  (backed up)
Iteration 17:{space 2}log pseudolikelihood = {res:-1288.5927}  (backed up)
Iteration 18:{space 2}log pseudolikelihood = {res:-1288.5907}  (backed up)
Iteration 19:{space 2}log pseudolikelihood = {res:-1288.5886}  (backed up)
Iteration 20:{space 2}log pseudolikelihood = {res:-1288.5865}  (backed up)
Iteration 21:{space 2}log pseudolikelihood = {res:-1288.5845}  (backed up)
Iteration 22:{space 2}log pseudolikelihood = {res:-1288.5824}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-1288.5804}  (backed up)
Iteration 24:{space 2}log pseudolikelihood = {res:-1288.5783}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-1288.5763}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:-1288.5742}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-1288.5722}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-1288.5701}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-1288.5681}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res: -1288.566}  (backed up)
Iteration 31:{space 2}log pseudolikelihood = {res: -1288.564}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-1288.5619}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-1288.5599}  (backed up)
Iteration 34:{space 2}log pseudolikelihood = {res:-1288.5579}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-1288.5558}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-1288.5538}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res:-1288.5518}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-1288.5498}  (backed up)
Iteration 39:{space 2}log pseudolikelihood = {res:-1288.5477}  (backed up)
Iteration 40:{space 2}log pseudolikelihood = {res:-1288.5457}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res:-1288.5437}  (backed up)
Iteration 42:{space 2}log pseudolikelihood = {res:-1288.5417}  (backed up)
Iteration 43:{space 2}log pseudolikelihood = {res:-1288.5396}  (backed up)
Iteration 44:{space 2}log pseudolikelihood = {res:-1288.5376}  (backed up)
Iteration 45:{space 2}log pseudolikelihood = {res:-1288.5356}  (backed up)
Iteration 46:{space 2}log pseudolikelihood = {res:-1288.5336}  (backed up)
Iteration 47:{space 2}log pseudolikelihood = {res:-1288.5316}  (backed up)
Iteration 48:{space 2}log pseudolikelihood = {res:-1288.5296}  (backed up)
Iteration 49:{space 2}log pseudolikelihood = {res:-1288.5276}  (backed up)
Iteration 50:{space 2}log pseudolikelihood = {res:-1288.5256}  (backed up)
Iteration 51:{space 2}log pseudolikelihood = {res:-1288.5236}  (backed up)
Iteration 52:{space 2}log pseudolikelihood = {res:-1288.5216}  (backed up)
Iteration 53:{space 2}log pseudolikelihood = {res:-1288.5196}  (backed up)
Iteration 54:{space 2}log pseudolikelihood = {res:-1288.5176}  (backed up)
Iteration 55:{space 2}log pseudolikelihood = {res:-1288.5156}  (backed up)
Iteration 56:{space 2}log pseudolikelihood = {res:-1288.5136}  (backed up)
Iteration 57:{space 2}log pseudolikelihood = {res:-1288.5116}  (backed up)
Iteration 58:{space 2}log pseudolikelihood = {res:-1288.5096}  (backed up)
Iteration 59:{space 2}log pseudolikelihood = {res:-1288.5076}  (backed up)
Iteration 60:{space 2}log pseudolikelihood = {res:-1288.5037}  (not concave)
Iteration 61:{space 2}log pseudolikelihood = {res:-1288.5036}  
Iteration 62:{space 2}log pseudolikelihood = {res:-1288.4957}  (not concave)
Iteration 63:{space 2}log pseudolikelihood = {res:-1288.4949}  
Iteration 64:{space 2}log pseudolikelihood = {res: -1288.491}  (not concave)
Iteration 65:{space 2}log pseudolikelihood = {res:-1288.4902}  
Iteration 66:{space 2}log pseudolikelihood = {res:-1288.3662}  (not concave)
Iteration 67:{space 2}log pseudolikelihood = {res:-1288.3646}  (not concave)
Iteration 68:{space 2}log pseudolikelihood = {res:-1288.3252}  (not concave)
Iteration 69:{space 2}log pseudolikelihood = {res:-1286.6349}  
Iteration 70:{space 2}log pseudolikelihood = {res:-1286.6244}  (not concave)
Iteration 71:{space 2}log pseudolikelihood = {res:-1286.6224}  
Iteration 72:{space 2}log pseudolikelihood = {res:-1286.6211}  (backed up)
Iteration 73:{space 2}log pseudolikelihood = {res:-1286.5029}  
Iteration 74:{space 2}log pseudolikelihood = {res:-1286.4579}  
Iteration 75:{space 2}log pseudolikelihood = {res:-1286.4578}  
Iteration 76:{space 2}log pseudolikelihood = {res:-1286.4577}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,716

{txt}{col 9}Grouping information
{col 9}{txt}{hline 16}{c TT}{hline 44}
{col 25}{txt}{c |}{col 31}No. of{col 44}Observations per group
{col 10}{txt}Group variable{col 25}{c |}{col 31}groups{col 41}Minimum{col 52}Average{col 63}Maximum
{col 9}{txt}{hline 16}{c +}{hline 44}
{col 19}{res}ccode{col 25}{txt}{c |}{res}{col 29}      13{col 39}       19{col 50}    208.9{col 61}      798
{col 12}{res}country_elec{col 25}{txt}{c |}{res}{col 29}      27{col 39}        8{col 50}    100.6{col 61}      296
{col 9}{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-1286.4577{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 92:(Std. err. adjusted for {res:13} clusters in {res:ccode})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8875766{col 40}{space 2} .1391792{col 51}{space 1}   -0.76{col 60}{space 3}0.447{col 68}{space 4} .6527255{col 81}{space 3} 1.206927
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9927188{col 40}{space 2} .0042071{col 51}{space 1}   -1.72{col 60}{space 3}0.085{col 68}{space 4} .9845071{col 81}{space 3} 1.000999
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.155348{col 40}{space 2}  .169831{col 51}{space 1}    0.98{col 60}{space 3}0.326{col 68}{space 4} .8661435{col 81}{space 3} 1.541117
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7485507{col 40}{space 2} .1450801{col 51}{space 1}   -1.49{col 60}{space 3}0.135{col 68}{space 4} .5119716{col 81}{space 3} 1.094452
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .6637163{col 40}{space 2} .0472031{col 51}{space 1}   -5.76{col 60}{space 3}0.000{col 68}{space 4} .5773586{col 81}{space 3}  .762991
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.101644{col 40}{space 2} .1364541{col 51}{space 1}    0.78{col 60}{space 3}0.434{col 68}{space 4} .8641876{col 81}{space 3} 1.404347
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.761234{col 40}{space 2} .2550319{col 51}{space 1}    3.91{col 60}{space 3}0.000{col 68}{space 4} 1.326052{col 81}{space 3} 2.339234
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.058276{col 40}{space 2} .0475326{col 51}{space 1}    1.26{col 60}{space 3}0.207{col 68}{space 4} .9690971{col 81}{space 3} 1.155662
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3908702{col 40}{space 2} .0438454{col 51}{space 1}   -8.37{col 60}{space 3}0.000{col 68}{space 4} .3137256{col 81}{space 3} .4869844
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.321118{col 40}{space 2} .4515938{col 51}{space 1}    0.81{col 60}{space 3}0.415{col 68}{space 4} .6760484{col 81}{space 3} 2.581697
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .9748291{col 40}{space 2} .0182535{col 51}{space 1}   -1.36{col 60}{space 3}0.173{col 68}{space 4} .9397015{col 81}{space 3}  1.01127
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .9840086{col 40}{space 2} .0149726{col 51}{space 1}   -1.06{col 60}{space 3}0.289{col 68}{space 4} .9550961{col 81}{space 3} 1.013796
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} .9683805{col 40}{space 2} .1184555{col 51}{space 1}   -0.26{col 60}{space 3}0.793{col 68}{space 4}  .761946{col 81}{space 3} 1.230744
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .9786545{col 40}{space 2} .4607136{col 51}{space 1}   -0.05{col 60}{space 3}0.963{col 68}{space 4} .3889698{col 81}{space 3} 2.462311
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                     {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 2.85e-30{col 40}{space 2} 2.86e-29{col 68}{space 4} 8.08e-39{col 81}{space 3} 1.00e-21
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode>country_elec        {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} .4546757{col 40}{space 2} .2068544{col 68}{space 4}  .186401{col 81}{space 3} 1.109061
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_countries = e(N_clust)

{txt}added scalar:
        e(N_countries) =  {res}13
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream==0 ///
> || ccode: || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-3539.2151}  
Iteration 1:{space 3}log likelihood = {res:-2399.5604}  
Iteration 2:{space 3}log likelihood = {res:-2321.8596}  
Iteration 3:{space 3}log likelihood = {res:-2315.0323}  
Iteration 4:{space 3}log likelihood = {res:-2314.9789}  
Iteration 5:{space 3}log likelihood = {res:-2314.9788}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2248.2454}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2248.2454}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2244.5076}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2243.4783}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2243.3934}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2243.3915}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2243.3915}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    24,275

{txt}{col 9}Grouping information
{col 9}{txt}{hline 16}{c TT}{hline 44}
{col 25}{txt}{c |}{col 31}No. of{col 44}Observations per group
{col 10}{txt}Group variable{col 25}{c |}{col 31}groups{col 41}Minimum{col 52}Average{col 63}Maximum
{col 9}{txt}{hline 16}{c +}{hline 44}
{col 19}{res}ccode{col 25}{txt}{c |}{res}{col 29}      15{col 39}       70{col 50}  1,618.3{col 61}    4,716
{col 12}{res}country_elec{col 25}{txt}{c |}{res}{col 29}      39{col 39}       70{col 50}    622.4{col 61}    1,256
{col 9}{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70} 61599.58
{txt}Log pseudolikelihood = {res}-2243.3915{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:15} clusters in {res:ccode})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .6795672{col 39}{space 2} .0416748{col 50}{space 1}   -6.30{col 59}{space 3}0.000{col 67}{space 4} .6026042{col 80}{space 3} .7663599
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9787157{col 39}{space 2}  .003547{col 50}{space 1}   -5.94{col 59}{space 3}0.000{col 67}{space 4} .9717883{col 80}{space 3} .9856924
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.726225{col 39}{space 2} .5715479{col 50}{space 1}    1.65{col 59}{space 3}0.099{col 67}{space 4} .9021255{col 80}{space 3} 3.303145
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 3.308077{col 39}{space 2} 1.053087{col 50}{space 1}    3.76{col 59}{space 3}0.000{col 67}{space 4} 1.772579{col 80}{space 3}   6.1737
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.149807{col 39}{space 2} .1442199{col 50}{space 1}    1.11{col 59}{space 3}0.266{col 67}{space 4} .8992058{col 80}{space 3} 1.470249
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2}  1.04084{col 39}{space 2} .1387308{col 50}{space 1}    0.30{col 59}{space 3}0.764{col 67}{space 4} .8015485{col 80}{space 3}  1.35157
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9490509{col 39}{space 2} .2077423{col 50}{space 1}   -0.24{col 59}{space 3}0.811{col 67}{space 4} .6179679{col 80}{space 3} 1.457515
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9112181{col 39}{space 2} .0334464{col 50}{space 1}   -2.53{col 59}{space 3}0.011{col 67}{space 4} .8479669{col 80}{space 3} .9791873
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5369772{col 39}{space 2} .0446962{col 50}{space 1}   -7.47{col 59}{space 3}0.000{col 67}{space 4} .4561469{col 80}{space 3} .6321308
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.510216{col 39}{space 2} .3434703{col 50}{space 1}    1.81{col 59}{space 3}0.070{col 67}{space 4} .9670507{col 80}{space 3} 2.358462
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9898898{col 39}{space 2} .0346951{col 50}{space 1}   -0.29{col 59}{space 3}0.772{col 67}{space 4} .9241717{col 80}{space 3} 1.060281
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.584975{col 39}{space 2} .2719239{col 50}{space 1}    2.68{col 59}{space 3}0.007{col 67}{space 4} 1.132368{col 80}{space 3} 2.218491
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .9271671{col 39}{space 2}  .198021{col 50}{space 1}   -0.35{col 59}{space 3}0.723{col 67}{space 4} .6100445{col 80}{space 3} 1.409141
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .0021299{col 39}{space 2} .0028492{col 50}{space 1}   -4.60{col 59}{space 3}0.000{col 67}{space 4} .0001548{col 80}{space 3} .0293112
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                    {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .8473489{col 39}{space 2}  .676943{col 67}{space 4} .1770278{col 80}{space 3} 4.055861
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode>country_elec       {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.126064{col 39}{space 2} .7922344{col 67}{space 4} .2835998{col 80}{space 3} 4.471158
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_countries = e(N_clust)

{txt}added scalar:
        e(N_countries) =  {res}15
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd if p_green_vs_mainstream==1 ///
> || ccode: || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-596.14212}  
Iteration 1:{space 3}log likelihood = {res:-595.45025}  
Iteration 2:{space 3}log likelihood = {res:-595.45005}  
Iteration 3:{space 3}log likelihood = {res:-595.45005}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-610.13551}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-610.13551}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-599.29752}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-598.51936}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-597.85082}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res: -597.2814}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-597.27428}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res:-597.27424}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 23:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 25:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res:-597.27422}  (not concave)
Iteration 27:{space 2}log pseudolikelihood = {res:-597.27421}  (not concave)
Iteration 28:{space 2}log pseudolikelihood = {res:-597.27421}  (not concave)
Iteration 29:{space 2}log pseudolikelihood = {res: -597.2742}  (not concave)
Iteration 30:{space 2}log pseudolikelihood = {res: -597.2742}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res: -597.2742}  (not concave)
Iteration 32:{space 2}log pseudolikelihood = {res:-597.27419}  (not concave)
Iteration 33:{space 2}log pseudolikelihood = {res:-597.27418}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-597.27409}  (not concave)
Iteration 35:{space 2}log pseudolikelihood = {res:-597.27401}  (not concave)
Iteration 36:{space 2}log pseudolikelihood = {res:-597.27399}  (not concave)
Iteration 37:{space 2}log pseudolikelihood = {res:-597.27395}  (not concave)
Iteration 38:{space 2}log pseudolikelihood = {res:-597.27242}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res:-597.27211}  (not concave)
Iteration 40:{space 2}log pseudolikelihood = {res:-597.26427}  (not concave)
Iteration 41:{space 2}log pseudolikelihood = {res: -597.2627}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res:-597.24258}  (not concave)
Iteration 43:{space 2}log pseudolikelihood = {res:-597.24157}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res:-596.31764}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res:-596.22474}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-596.07479}  (not concave)
Iteration 47:{space 2}log pseudolikelihood = {res:-595.60563}  
Iteration 48:{space 2}log pseudolikelihood = {res:-595.49409}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-595.48301}  (not concave)
Iteration 50:{space 2}log pseudolikelihood = {res:-595.47859}  (not concave)
Iteration 51:{space 2}log pseudolikelihood = {res:-595.46458}  (not concave)
Iteration 52:{space 2}log pseudolikelihood = {res:-595.46178}  (not concave)
Iteration 53:{space 2}log pseudolikelihood = {res:-595.45955}  
Iteration 54:{space 2}log pseudolikelihood = {res:-595.45818}  (backed up)
Iteration 55:{space 2}log pseudolikelihood = {res:-595.45801}  (backed up)
Iteration 56:{space 2}log pseudolikelihood = {res:-595.45792}  (backed up)
Iteration 57:{space 2}log pseudolikelihood = {res:-595.45788}  (backed up)
Iteration 58:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 59:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 60:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 61:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 62:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 63:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 64:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 65:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 66:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 67:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 68:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 69:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 70:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 71:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 72:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 73:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 74:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 75:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 76:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 77:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 78:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 79:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 80:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 81:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 82:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 83:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 84:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 85:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 86:{space 2}log pseudolikelihood = {res:-595.45787}  (not concave)
Iteration 87:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 88:{space 2}log pseudolikelihood = {res:-595.45787}  (backed up)
Iteration 89:{space 2}log pseudolikelihood = {res:-595.45785}  (backed up)
Iteration 90:{space 2}log pseudolikelihood = {res:-595.45784}  (backed up)
Iteration 91:{space 2}log pseudolikelihood = {res:-595.45781}  (not concave)
Iteration 92:{space 2}log pseudolikelihood = {res: -595.4578}  
Iteration 93:{space 2}log pseudolikelihood = {res:-595.45686}  
Iteration 94:{space 2}log pseudolikelihood = {res:-595.45005}  
Iteration 95:{space 2}log pseudolikelihood = {res:-595.45005}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,045

{txt}{col 9}Grouping information
{col 9}{txt}{hline 16}{c TT}{hline 44}
{col 25}{txt}{c |}{col 31}No. of{col 44}Observations per group
{col 10}{txt}Group variable{col 25}{c |}{col 31}groups{col 41}Minimum{col 52}Average{col 63}Maximum
{col 9}{txt}{hline 16}{c +}{hline 44}
{col 19}{res}ccode{col 25}{txt}{c |}{res}{col 29}      10{col 39}        3{col 50}    104.5{col 61}      307
{col 12}{res}country_elec{col 25}{txt}{c |}{res}{col 29}      21{col 39}        3{col 50}     49.8{col 61}      177
{col 9}{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-595.45005{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 91:(Std. err. adjusted for {res:10} clusters in {res:ccode})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .793468{col 39}{space 2} .1434028{col 50}{space 1}   -1.28{col 59}{space 3}0.201{col 67}{space 4} .5567913{col 80}{space 3} 1.130749
{txt}{space 22}age {c |}{col 27}{res}{space 2} 1.000777{col 39}{space 2} .0057825{col 50}{space 1}    0.13{col 59}{space 3}0.893{col 67}{space 4} .9895077{col 80}{space 3} 1.012175
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .9540044{col 39}{space 2} .3108488{col 50}{space 1}   -0.14{col 59}{space 3}0.885{col 67}{space 4} .5037307{col 80}{space 3} 1.806768
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}   .85086{col 39}{space 2}   .30241{col 50}{space 1}   -0.45{col 59}{space 3}0.650{col 67}{space 4} .4239618{col 80}{space 3} 1.707613
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.206687{col 39}{space 2} .1500872{col 50}{space 1}    1.51{col 59}{space 3}0.131{col 67}{space 4} .9456331{col 80}{space 3} 1.539809
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9538301{col 39}{space 2} .1449142{col 50}{space 1}   -0.31{col 59}{space 3}0.756{col 67}{space 4} .7081889{col 80}{space 3} 1.284674
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.003588{col 39}{space 2} .2045858{col 50}{space 1}    0.02{col 59}{space 3}0.986{col 67}{space 4} .6730297{col 80}{space 3}   1.4965
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.257671{col 39}{space 2} .0607032{col 50}{space 1}    4.75{col 59}{space 3}0.000{col 67}{space 4}  1.14415{col 80}{space 3} 1.382457
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5579736{col 39}{space 2} .1311957{col 50}{space 1}   -2.48{col 59}{space 3}0.013{col 67}{space 4} .3519424{col 80}{space 3} .8846178
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .9346775{col 39}{space 2} .1749268{col 50}{space 1}   -0.36{col 59}{space 3}0.718{col 67}{space 4} .6476759{col 80}{space 3} 1.348857
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.008672{col 39}{space 2} .0104566{col 50}{space 1}    0.83{col 59}{space 3}0.405{col 67}{space 4} .9883846{col 80}{space 3} 1.029377
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .9285604{col 39}{space 2} .0300266{col 50}{space 1}   -2.29{col 59}{space 3}0.022{col 67}{space 4} .8715356{col 80}{space 3} .9893163
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2}   1.0505{col 39}{space 2} .0676862{col 50}{space 1}    0.76{col 59}{space 3}0.444{col 67}{space 4} .9258726{col 80}{space 3} 1.191903
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} .6125575{col 39}{space 2} .2751731{col 50}{space 1}   -1.09{col 59}{space 3}0.275{col 67}{space 4} .2539628{col 80}{space 3} 1.477487
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                    {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 4.38e-32{col 39}{space 2} 1.77e-31{col 67}{space 4} 1.59e-35{col 80}{space 3} 1.20e-28
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode>country_elec       {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.28e-34{col 39}{space 2} 1.21e-34{col 67}{space 4} 1.98e-35{col 80}{space 3} 8.21e-34
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_countries = e(N_clust)

{txt}added scalar:
        e(N_countries) =  {res}10
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea18.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_countries) title(Table A18. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea18.rtf"'})

{com}. 
. *************
. **Table A19**
. *************
. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd i.country_elec if p_niche==0 ///
> || ccode:, or vce(robust)
{res}{txt}note: {bf:22.country_elec} != 0 predicts failure perfectly;
      {bf:22.country_elec} omitted and 379 obs not used.

note: {bf:7.country_elec} identifies no observations in the sample.
note: {bf:27.country_elec} identifies no observations in the sample.
note: {bf:28.country_elec} identifies no observations in the sample.
note: {bf:29.country_elec} identifies no observations in the sample.
note: {bf:31.country_elec} identifies no observations in the sample.
note: {bf:33.country_elec} identifies no observations in the sample.
note: {bf:40.country_elec} identifies no observations in the sample.
note: {bf:44.country_elec} identifies no observations in the sample.
note: {bf:53.country_elec} identifies no observations in the sample.
note: {bf:54.country_elec} identifies no observations in the sample.
note: {bf:55.country_elec} identifies no observations in the sample.
note: {bf:56.country_elec} identifies no observations in the sample.
note: {bf:57.country_elec} identifies no observations in the sample.
note: {bf:58.country_elec} identifies no observations in the sample.
note: {bf:59.country_elec} identifies no observations in the sample.
note: {bf:60.country_elec} identifies no observations in the sample.
note: {bf:61.country_elec} identifies no observations in the sample.
note: {bf:69.country_elec} identifies no observations in the sample.
note: {bf:70.country_elec} identifies no observations in the sample.
note: {bf:71.country_elec} identifies no observations in the sample.
note: {bf:76.country_elec} identifies no observations in the sample.
note: {bf:78.country_elec} identifies no observations in the sample.
note: {bf:79.country_elec} identifies no observations in the sample.
note: {bf:82.country_elec} identifies no observations in the sample.
note: {bf:86.country_elec} identifies no observations in the sample.
note: {bf:88.country_elec} identifies no observations in the sample.
note: {bf:89.country_elec} identifies no observations in the sample.
note: {bf:92.country_elec} identifies no observations in the sample.
note: {bf:93.country_elec} identifies no observations in the sample.
note: {bf:96.country_elec} identifies no observations in the sample.
note: {bf:98.country_elec} identifies no observations in the sample.
note: {bf:104.country_elec} identifies no observations in the sample.
note: {bf:105.country_elec} identifies no observations in the sample.
note: {bf:106.country_elec} identifies no observations in the sample.
note: {bf:107.country_elec} identifies no observations in the sample.
note: {bf:108.country_elec} identifies no observations in the sample.
note: {bf:109.country_elec} identifies no observations in the sample.
note: {bf:110.country_elec} identifies no observations in the sample.
note: {bf:111.country_elec} identifies no observations in the sample.
note: {bf:112.country_elec} identifies no observations in the sample.
note: {bf:113.country_elec} identifies no observations in the sample.
note: {bf:114.country_elec} identifies no observations in the sample.
note: {bf:115.country_elec} identifies no observations in the sample.
note: {bf:116.country_elec} identifies no observations in the sample.
note: {bf:117.country_elec} identifies no observations in the sample.
note: {bf:121.country_elec} identifies no observations in the sample.
note: {bf:122.country_elec} identifies no observations in the sample.
note: {bf:123.country_elec} identifies no observations in the sample.
note: {bf:124.country_elec} identifies no observations in the sample.
note: {bf:125.country_elec} identifies no observations in the sample.
note: {bf:126.country_elec} identifies no observations in the sample.
note: {bf:127.country_elec} identifies no observations in the sample.
note: {bf:128.country_elec} identifies no observations in the sample.
note: {bf:129.country_elec} identifies no observations in the sample.
note: {bf:131.country_elec} identifies no observations in the sample.
note: {bf:134.country_elec} identifies no observations in the sample.
note: {bf:135.country_elec} identifies no observations in the sample.
note: {bf:136.country_elec} identifies no observations in the sample.
note: {bf:137.country_elec} identifies no observations in the sample.
note: {bf:139.country_elec} identifies no observations in the sample.
note: {bf:140.country_elec} identifies no observations in the sample.
note: {bf:141.country_elec} identifies no observations in the sample.
note: {bf:142.country_elec} identifies no observations in the sample.
note: {bf:143.country_elec} identifies no observations in the sample.
note: {bf:149.country_elec} identifies no observations in the sample.
note: {bf:150.country_elec} identifies no observations in the sample.
note: {bf:151.country_elec} identifies no observations in the sample.
note: {bf:152.country_elec} identifies no observations in the sample.
note: {bf:155.country_elec} identifies no observations in the sample.
note: {bf:156.country_elec} identifies no observations in the sample.
note: {bf:157.country_elec} identifies no observations in the sample.
note: {bf:164.country_elec} identifies no observations in the sample.
note: {bf:168.country_elec} omitted because of collinearity.
note: {bf:169.country_elec} omitted because of collinearity.
note: {bf:170.country_elec} identifies no observations in the sample.
note: {bf:171.country_elec} omitted because of collinearity.

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-7163.7806}  
Iteration 1:{space 3}log likelihood = {res: -6708.631}  
Iteration 2:{space 3}log likelihood = {res:-6696.1709}  
Iteration 3:{space 3}log likelihood = {res:-6695.8394}  
Iteration 4:{space 3}log likelihood = {res:-6695.8392}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6728.0354}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6728.0354}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6721.8541}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-6715.6496}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-6707.2165}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res: -6702.505}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-6696.4102}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res:-6696.0945}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-6695.9663}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-6695.8624}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res:-6695.8416}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res:-6695.8411}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 29:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 30:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 31:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 32:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 33:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 34:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 36:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 37:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 40:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 43:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 45:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 47:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 48:{space 2}log pseudolikelihood = {res:-6695.8408}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-6695.8408}  (backed up)
Iteration 50:{space 2}log pseudolikelihood = {res:-6695.8407}  (backed up)
Iteration 51:{space 2}log pseudolikelihood = {res:-6695.8407}  (backed up)
Iteration 52:{space 2}log pseudolikelihood = {res:-6695.8406}  
Iteration 53:{space 2}log pseudolikelihood = {res:-6695.8406}  (backed up)
Iteration 54:{space 2}log pseudolikelihood = {res:-6695.8392}  
Iteration 55:{space 2}log pseudolikelihood = {res:-6695.8392}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,493
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        15

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}   1,699.5
{col 63}{txt}max{col 67}={res}{col 69}     5,125

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-6695.8392{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 91:(Std. err. adjusted for {res:15} clusters in {res:ccode})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.085568{col 39}{space 2} .0630643{col 50}{space 1}    1.41{col 59}{space 3}0.158{col 67}{space 4} .9687413{col 80}{space 3} 1.216483
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9842301{col 39}{space 2} .0025388{col 50}{space 1}   -6.16{col 59}{space 3}0.000{col 67}{space 4} .9792667{col 80}{space 3} .9892186
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.233576{col 39}{space 2} .1078936{col 50}{space 1}    2.40{col 59}{space 3}0.016{col 67}{space 4} 1.039241{col 80}{space 3}  1.46425
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.324096{col 39}{space 2}  .168253{col 50}{space 1}    2.21{col 59}{space 3}0.027{col 67}{space 4} 1.032184{col 80}{space 3} 1.698563
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8149528{col 39}{space 2} .0607389{col 50}{space 1}   -2.75{col 59}{space 3}0.006{col 67}{space 4} .7041932{col 80}{space 3} .9431332
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6502049{col 39}{space 2} .0611193{col 50}{space 1}   -4.58{col 59}{space 3}0.000{col 67}{space 4} .5408008{col 80}{space 3} .7817416
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.099807{col 39}{space 2} .2329962{col 50}{space 1}    6.69{col 59}{space 3}0.000{col 67}{space 4} 1.689388{col 80}{space 3} 2.609933
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9733529{col 39}{space 2} .0377836{col 50}{space 1}   -0.70{col 59}{space 3}0.487{col 67}{space 4} .9020454{col 80}{space 3} 1.050297
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3948399{col 39}{space 2} .0635624{col 50}{space 1}   -5.77{col 59}{space 3}0.000{col 67}{space 4} .2879999{col 80}{space 3} .5413146
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.212461{col 39}{space 2}  .201276{col 50}{space 1}    1.16{col 59}{space 3}0.246{col 67}{space 4} .8757152{col 80}{space 3} 1.678697
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.088106{col 39}{space 2}   .00701{col 50}{space 1}   13.11{col 59}{space 3}0.000{col 67}{space 4} 1.074453{col 80}{space 3} 1.101932
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9968759{col 39}{space 2} .0042963{col 50}{space 1}   -0.73{col 59}{space 3}0.468{col 67}{space 4} .9884908{col 80}{space 3} 1.005332
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.549393{col 39}{space 2} .0507234{col 50}{space 1}   13.37{col 59}{space 3}0.000{col 67}{space 4}   1.4531{col 80}{space 3} 1.652068
{txt}{space 25} {c |}
{space 13}country_elec {c |}
{space 23}7  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}22  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}23  {c |}{col 27}{res}{space 2} 2.168598{col 39}{space 2}  .065622{col 50}{space 1}   25.58{col 59}{space 3}0.000{col 67}{space 4} 2.043721{col 80}{space 3} 2.301105
{txt}{space 22}24  {c |}{col 27}{res}{space 2} 1.342169{col 39}{space 2} .1417317{col 50}{space 1}    2.79{col 59}{space 3}0.005{col 67}{space 4} 1.091243{col 80}{space 3} 1.650796
{txt}{space 22}25  {c |}{col 27}{res}{space 2} 1.935606{col 39}{space 2} .2182727{col 50}{space 1}    5.86{col 59}{space 3}0.000{col 67}{space 4} 1.551778{col 80}{space 3} 2.414374
{txt}{space 22}26  {c |}{col 27}{res}{space 2}  1.38966{col 39}{space 2} .1186137{col 50}{space 1}    3.86{col 59}{space 3}0.000{col 67}{space 4} 1.175587{col 80}{space 3} 1.642715
{txt}{space 22}27  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}28  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}29  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}30  {c |}{col 27}{res}{space 2} .1175463{col 39}{space 2} .0234745{col 50}{space 1}  -10.72{col 59}{space 3}0.000{col 67}{space 4} .0794732{col 80}{space 3} .1738592
{txt}{space 22}31  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}32  {c |}{col 27}{res}{space 2} .1522368{col 39}{space 2}  .016027{col 50}{space 1}  -17.88{col 59}{space 3}0.000{col 67}{space 4} .1238535{col 80}{space 3} .1871248
{txt}{space 22}33  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}34  {c |}{col 27}{res}{space 2} .3239175{col 39}{space 2} .0144029{col 50}{space 1}  -25.35{col 59}{space 3}0.000{col 67}{space 4} .2968834{col 80}{space 3} .3534132
{txt}{space 22}35  {c |}{col 27}{res}{space 2} .7046104{col 39}{space 2} .0599625{col 50}{space 1}   -4.11{col 59}{space 3}0.000{col 67}{space 4} .5963642{col 80}{space 3} .8325043
{txt}{space 22}36  {c |}{col 27}{res}{space 2} 5.129066{col 39}{space 2}  .245125{col 50}{space 1}   34.21{col 59}{space 3}0.000{col 67}{space 4} 4.670445{col 80}{space 3} 5.632723
{txt}{space 22}37  {c |}{col 27}{res}{space 2} 4.305023{col 39}{space 2} .2014277{col 50}{space 1}   31.20{col 59}{space 3}0.000{col 67}{space 4} 3.927793{col 80}{space 3} 4.718482
{txt}{space 22}40  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}43  {c |}{col 27}{res}{space 2} .5879729{col 39}{space 2}  .092801{col 50}{space 1}   -3.36{col 59}{space 3}0.001{col 67}{space 4} .4315294{col 80}{space 3} .8011323
{txt}{space 22}44  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}45  {c |}{col 27}{res}{space 2} .5912425{col 39}{space 2} .0919535{col 50}{space 1}   -3.38{col 59}{space 3}0.001{col 67}{space 4} .4358949{col 80}{space 3} .8019542
{txt}{space 22}48  {c |}{col 27}{res}{space 2} .7989628{col 39}{space 2} .0488306{col 50}{space 1}   -3.67{col 59}{space 3}0.000{col 67}{space 4} .7087666{col 80}{space 3} .9006371
{txt}{space 22}50  {c |}{col 27}{res}{space 2} .3042111{col 39}{space 2} .0184527{col 50}{space 1}  -19.62{col 59}{space 3}0.000{col 67}{space 4} .2701117{col 80}{space 3} .3426153
{txt}{space 22}51  {c |}{col 27}{res}{space 2} 1.255874{col 39}{space 2} .0743073{col 50}{space 1}    3.85{col 59}{space 3}0.000{col 67}{space 4} 1.118362{col 80}{space 3} 1.410294
{txt}{space 22}53  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}54  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}55  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}56  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}57  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}58  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}59  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}60  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}61  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}62  {c |}{col 27}{res}{space 2} .4594589{col 39}{space 2} .0578332{col 50}{space 1}   -6.18{col 59}{space 3}0.000{col 67}{space 4} .3590077{col 80}{space 3} .5880165
{txt}{space 22}64  {c |}{col 27}{res}{space 2}  1.35231{col 39}{space 2} .0930919{col 50}{space 1}    4.38{col 59}{space 3}0.000{col 67}{space 4} 1.181627{col 80}{space 3} 1.547648
{txt}{space 22}65  {c |}{col 27}{res}{space 2}  1.42355{col 39}{space 2} .0903692{col 50}{space 1}    5.56{col 59}{space 3}0.000{col 67}{space 4} 1.257005{col 80}{space 3}  1.61216
{txt}{space 22}66  {c |}{col 27}{res}{space 2} 1.433618{col 39}{space 2} .1140057{col 50}{space 1}    4.53{col 59}{space 3}0.000{col 67}{space 4} 1.226714{col 80}{space 3}  1.67542
{txt}{space 22}67  {c |}{col 27}{res}{space 2} 1.364813{col 39}{space 2} .2552005{col 50}{space 1}    1.66{col 59}{space 3}0.096{col 67}{space 4} .9460426{col 80}{space 3} 1.968954
{txt}{space 22}69  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}70  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}71  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}74  {c |}{col 27}{res}{space 2} 1.389981{col 39}{space 2} .1480767{col 50}{space 1}    3.09{col 59}{space 3}0.002{col 67}{space 4} 1.128052{col 80}{space 3} 1.712729
{txt}{space 22}76  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}78  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}79  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}82  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}84  {c |}{col 27}{res}{space 2} .1203489{col 39}{space 2}  .024484{col 50}{space 1}  -10.41{col 59}{space 3}0.000{col 67}{space 4}  .080774{col 80}{space 3} .1793132
{txt}{space 22}86  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}88  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}89  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}92  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}93  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}96  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}98  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}101  {c |}{col 27}{res}{space 2} .5838072{col 39}{space 2} .1031846{col 50}{space 1}   -3.04{col 59}{space 3}0.002{col 67}{space 4} .4128806{col 80}{space 3}  .825495
{txt}{space 21}102  {c |}{col 27}{res}{space 2} 1.598551{col 39}{space 2} .2238842{col 50}{space 1}    3.35{col 59}{space 3}0.001{col 67}{space 4}  1.21482{col 80}{space 3} 2.103493
{txt}{space 21}104  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}105  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}106  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}107  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}108  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}109  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}110  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}111  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}112  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}113  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}114  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}115  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}116  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}117  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}118  {c |}{col 27}{res}{space 2} .3421097{col 39}{space 2} .0383896{col 50}{space 1}   -9.56{col 59}{space 3}0.000{col 67}{space 4}  .274567{col 80}{space 3} .4262677
{txt}{space 21}119  {c |}{col 27}{res}{space 2} .9072704{col 39}{space 2} .0420715{col 50}{space 1}   -2.10{col 59}{space 3}0.036{col 67}{space 4}  .828448{col 80}{space 3} .9935923
{txt}{space 21}120  {c |}{col 27}{res}{space 2} .8953921{col 39}{space 2} .0760665{col 50}{space 1}   -1.30{col 59}{space 3}0.193{col 67}{space 4} .7580554{col 80}{space 3}  1.05761
{txt}{space 21}121  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}122  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}123  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}124  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}125  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}126  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}127  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}128  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}129  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}130  {c |}{col 27}{res}{space 2} .9383308{col 39}{space 2} .0637454{col 50}{space 1}   -0.94{col 59}{space 3}0.349{col 67}{space 4} .8213527{col 80}{space 3} 1.071969
{txt}{space 21}131  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}133  {c |}{col 27}{res}{space 2} 3.626752{col 39}{space 2} .3521096{col 50}{space 1}   13.27{col 59}{space 3}0.000{col 67}{space 4} 2.998316{col 80}{space 3} 4.386905
{txt}{space 21}134  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}135  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}136  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}137  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}138  {c |}{col 27}{res}{space 2} .1384165{col 39}{space 2} .0257956{col 50}{space 1}  -10.61{col 59}{space 3}0.000{col 67}{space 4}  .096063{col 80}{space 3} .1994432
{txt}{space 21}139  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}140  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}141  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}142  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}143  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}144  {c |}{col 27}{res}{space 2} .2726022{col 39}{space 2} .0245708{col 50}{space 1}  -14.42{col 59}{space 3}0.000{col 67}{space 4} .2284583{col 80}{space 3} .3252758
{txt}{space 21}145  {c |}{col 27}{res}{space 2} .8439294{col 39}{space 2} .0980872{col 50}{space 1}   -1.46{col 59}{space 3}0.144{col 67}{space 4} .6720069{col 80}{space 3} 1.059836
{txt}{space 21}146  {c |}{col 27}{res}{space 2} .7844438{col 39}{space 2} .0758023{col 50}{space 1}   -2.51{col 59}{space 3}0.012{col 67}{space 4} .6490956{col 80}{space 3} .9480146
{txt}{space 21}147  {c |}{col 27}{res}{space 2} 2.056234{col 39}{space 2} .1214192{col 50}{space 1}   12.21{col 59}{space 3}0.000{col 67}{space 4} 1.831512{col 80}{space 3}  2.30853
{txt}{space 21}149  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}150  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}151  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}152  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}155  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}156  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}157  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}164  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}168  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}169  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}170  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}171  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} .0538643{col 39}{space 2} .0122296{col 50}{space 1}  -12.87{col 59}{space 3}0.000{col 67}{space 4} .0345176{col 80}{space 3} .0840546
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                    {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.62e-33{col 39}{space 2} 5.90e-34{col 67}{space 4} 7.93e-34{col 80}{space 3} 3.31e-33
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}15
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd i.country_elec if p_niche==1 ///
> || ccode:, or vce(robust)
{res}{txt}note: {bf:25.country_elec} != 0 predicts success perfectly;
      {bf:25.country_elec} omitted and 14 obs not used.

note: {bf:7.country_elec} identifies no observations in the sample.
note: {bf:22.country_elec} identifies no observations in the sample.
note: {bf:27.country_elec} identifies no observations in the sample.
note: {bf:28.country_elec} identifies no observations in the sample.
note: {bf:29.country_elec} identifies no observations in the sample.
note: {bf:30.country_elec} identifies no observations in the sample.
note: {bf:31.country_elec} identifies no observations in the sample.
note: {bf:32.country_elec} identifies no observations in the sample.
note: {bf:33.country_elec} identifies no observations in the sample.
note: {bf:34.country_elec} identifies no observations in the sample.
note: {bf:35.country_elec} identifies no observations in the sample.
note: {bf:40.country_elec} identifies no observations in the sample.
note: {bf:44.country_elec} identifies no observations in the sample.
note: {bf:53.country_elec} identifies no observations in the sample.
note: {bf:59.country_elec} identifies no observations in the sample.
note: {bf:60.country_elec} identifies no observations in the sample.
note: {bf:61.country_elec} identifies no observations in the sample.
note: {bf:65.country_elec} identifies no observations in the sample.
note: {bf:66.country_elec} identifies no observations in the sample.
note: {bf:69.country_elec} identifies no observations in the sample.
note: {bf:70.country_elec} identifies no observations in the sample.
note: {bf:71.country_elec} identifies no observations in the sample.
note: {bf:76.country_elec} identifies no observations in the sample.
note: {bf:78.country_elec} identifies no observations in the sample.
note: {bf:79.country_elec} identifies no observations in the sample.
note: {bf:82.country_elec} identifies no observations in the sample.
note: {bf:86.country_elec} identifies no observations in the sample.
note: {bf:88.country_elec} identifies no observations in the sample.
note: {bf:89.country_elec} identifies no observations in the sample.
note: {bf:92.country_elec} identifies no observations in the sample.
note: {bf:93.country_elec} identifies no observations in the sample.
note: {bf:96.country_elec} identifies no observations in the sample.
note: {bf:98.country_elec} identifies no observations in the sample.
note: {bf:104.country_elec} identifies no observations in the sample.
note: {bf:105.country_elec} identifies no observations in the sample.
note: {bf:106.country_elec} identifies no observations in the sample.
note: {bf:107.country_elec} identifies no observations in the sample.
note: {bf:108.country_elec} identifies no observations in the sample.
note: {bf:109.country_elec} identifies no observations in the sample.
note: {bf:110.country_elec} identifies no observations in the sample.
note: {bf:111.country_elec} identifies no observations in the sample.
note: {bf:112.country_elec} identifies no observations in the sample.
note: {bf:113.country_elec} identifies no observations in the sample.
note: {bf:114.country_elec} identifies no observations in the sample.
note: {bf:115.country_elec} identifies no observations in the sample.
note: {bf:116.country_elec} identifies no observations in the sample.
note: {bf:117.country_elec} identifies no observations in the sample.
note: {bf:121.country_elec} identifies no observations in the sample.
note: {bf:122.country_elec} identifies no observations in the sample.
note: {bf:123.country_elec} identifies no observations in the sample.
note: {bf:124.country_elec} identifies no observations in the sample.
note: {bf:125.country_elec} identifies no observations in the sample.
note: {bf:126.country_elec} identifies no observations in the sample.
note: {bf:127.country_elec} identifies no observations in the sample.
note: {bf:128.country_elec} identifies no observations in the sample.
note: {bf:129.country_elec} identifies no observations in the sample.
note: {bf:131.country_elec} identifies no observations in the sample.
note: {bf:134.country_elec} identifies no observations in the sample.
note: {bf:135.country_elec} identifies no observations in the sample.
note: {bf:136.country_elec} identifies no observations in the sample.
note: {bf:137.country_elec} identifies no observations in the sample.
note: {bf:139.country_elec} identifies no observations in the sample.
note: {bf:140.country_elec} identifies no observations in the sample.
note: {bf:141.country_elec} identifies no observations in the sample.
note: {bf:142.country_elec} identifies no observations in the sample.
note: {bf:143.country_elec} identifies no observations in the sample.
note: {bf:149.country_elec} identifies no observations in the sample.
note: {bf:150.country_elec} identifies no observations in the sample.
note: {bf:151.country_elec} identifies no observations in the sample.
note: {bf:152.country_elec} identifies no observations in the sample.
note: {bf:156.country_elec} identifies no observations in the sample.
note: {bf:157.country_elec} identifies no observations in the sample.
note: {bf:164.country_elec} identifies no observations in the sample.
note: {bf:168.country_elec} omitted because of collinearity.
note: {bf:169.country_elec} omitted because of collinearity.
note: {bf:170.country_elec} identifies no observations in the sample.
note: {bf:171.country_elec} omitted because of collinearity.

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2194.9243}  
Iteration 1:{space 3}log likelihood = {res: -2187.081}  
Iteration 2:{space 3}log likelihood = {res:-2187.0466}  
Iteration 3:{space 3}log likelihood = {res:-2187.0466}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2212.5301}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2212.5301}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2202.2595}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-2191.2105}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-2188.3996}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-2187.1096}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-2187.0773}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res:-2187.0513}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-2187.0487}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-2187.0482}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 27:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 32:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 36:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 37:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 40:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 41:{space 2}log pseudolikelihood = {res:-2187.0481}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 43:{space 2}log pseudolikelihood = {res:-2187.0481}  (backed up)
Iteration 44:{space 2}log pseudolikelihood = {res: -2187.048}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res: -2187.048}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res: -2187.048}  
Iteration 47:{space 2}log pseudolikelihood = {res: -2187.048}  (backed up)
Iteration 48:{space 2}log pseudolikelihood = {res: -2187.048}  (backed up)
Iteration 49:{space 2}log pseudolikelihood = {res: -2187.048}  (backed up)
Iteration 50:{space 2}log pseudolikelihood = {res: -2187.047}  
Iteration 51:{space 2}log pseudolikelihood = {res:-2187.0466}  
Iteration 52:{space 2}log pseudolikelihood = {res:-2187.0466}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,501
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        15

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     300.1
{col 63}{txt}max{col 67}={res}{col 69}       987

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-2187.0466{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 91:(Std. err. adjusted for {res:15} clusters in {res:ccode})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .827598{col 39}{space 2} .0950891{col 50}{space 1}   -1.65{col 59}{space 3}0.100{col 67}{space 4} .6607213{col 80}{space 3} 1.036622
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9925799{col 39}{space 2} .0028708{col 50}{space 1}   -2.58{col 59}{space 3}0.010{col 67}{space 4} .9869692{col 80}{space 3} .9982224
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.276119{col 39}{space 2} .2201902{col 50}{space 1}    1.41{col 59}{space 3}0.158{col 67}{space 4} .9099538{col 80}{space 3}  1.78963
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.020037{col 39}{space 2} .1611338{col 50}{space 1}    0.13{col 59}{space 3}0.900{col 67}{space 4} .7484332{col 80}{space 3} 1.390206
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.130896{col 39}{space 2} .0922903{col 50}{space 1}    1.51{col 59}{space 3}0.132{col 67}{space 4}  .963735{col 80}{space 3} 1.327051
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.630168{col 39}{space 2} .1999258{col 50}{space 1}    3.98{col 59}{space 3}0.000{col 67}{space 4} 1.281859{col 80}{space 3} 2.073122
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .8163249{col 39}{space 2} .0680843{col 50}{space 1}   -2.43{col 59}{space 3}0.015{col 67}{space 4} .6932182{col 80}{space 3} .9612938
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.108064{col 39}{space 2}  .037516{col 50}{space 1}    3.03{col 59}{space 3}0.002{col 67}{space 4} 1.036921{col 80}{space 3} 1.184089
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4296093{col 39}{space 2} .0447257{col 50}{space 1}   -8.12{col 59}{space 3}0.000{col 67}{space 4} .3503136{col 80}{space 3} .5268542
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7597405{col 39}{space 2} .1111986{col 50}{space 1}   -1.88{col 59}{space 3}0.060{col 67}{space 4} .5702695{col 80}{space 3} 1.012163
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.059683{col 39}{space 2} .0189928{col 50}{space 1}    3.23{col 59}{space 3}0.001{col 67}{space 4} 1.023104{col 80}{space 3}  1.09757
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .8973796{col 39}{space 2} .0138338{col 50}{space 1}   -7.02{col 59}{space 3}0.000{col 67}{space 4} .8706714{col 80}{space 3} .9249072
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 2.307935{col 39}{space 2} .2264651{col 50}{space 1}    8.52{col 59}{space 3}0.000{col 67}{space 4} 1.904144{col 80}{space 3} 2.797354
{txt}{space 25} {c |}
{space 13}country_elec {c |}
{space 23}7  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}22  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}23  {c |}{col 27}{res}{space 2} .1333502{col 39}{space 2} .0120121{col 50}{space 1}  -22.37{col 59}{space 3}0.000{col 67}{space 4} .1117683{col 80}{space 3} .1590996
{txt}{space 22}24  {c |}{col 27}{res}{space 2} .0370749{col 39}{space 2} .0022841{col 50}{space 1}  -53.48{col 59}{space 3}0.000{col 67}{space 4} .0328578{col 80}{space 3} .0418332
{txt}{space 22}25  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}26  {c |}{col 27}{res}{space 2} .0702791{col 39}{space 2} .0066473{col 50}{space 1}  -28.07{col 59}{space 3}0.000{col 67}{space 4}  .058387{col 80}{space 3} .0845934
{txt}{space 22}27  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}28  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}29  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}30  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}31  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}32  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}33  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}34  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}35  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}36  {c |}{col 27}{res}{space 2} .2598114{col 39}{space 2} .0534239{col 50}{space 1}   -6.55{col 59}{space 3}0.000{col 67}{space 4} .1736318{col 80}{space 3}  .388765
{txt}{space 22}37  {c |}{col 27}{res}{space 2} .3305264{col 39}{space 2} .0847083{col 50}{space 1}   -4.32{col 59}{space 3}0.000{col 67}{space 4} .2000128{col 80}{space 3} .5462036
{txt}{space 22}40  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}43  {c |}{col 27}{res}{space 2}  .304638{col 39}{space 2} .0476968{col 50}{space 1}   -7.59{col 59}{space 3}0.000{col 67}{space 4} .2241365{col 80}{space 3} .4140527
{txt}{space 22}44  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}45  {c |}{col 27}{res}{space 2} 1.537062{col 39}{space 2} .3058176{col 50}{space 1}    2.16{col 59}{space 3}0.031{col 67}{space 4} 1.040721{col 80}{space 3} 2.270118
{txt}{space 22}48  {c |}{col 27}{res}{space 2} 2.478962{col 39}{space 2} .7104603{col 50}{space 1}    3.17{col 59}{space 3}0.002{col 67}{space 4} 1.413576{col 80}{space 3} 4.347311
{txt}{space 22}50  {c |}{col 27}{res}{space 2} .0291353{col 39}{space 2} .0037401{col 50}{space 1}  -27.54{col 59}{space 3}0.000{col 67}{space 4} .0226543{col 80}{space 3} .0374703
{txt}{space 22}51  {c |}{col 27}{res}{space 2}  .072459{col 39}{space 2} .0050178{col 50}{space 1}  -37.90{col 59}{space 3}0.000{col 67}{space 4} .0632626{col 80}{space 3} .0829923
{txt}{space 22}53  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}59  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}60  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}61  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}62  {c |}{col 27}{res}{space 2}  .029385{col 39}{space 2} .0048817{col 50}{space 1}  -21.23{col 59}{space 3}0.000{col 67}{space 4} .0212186{col 80}{space 3} .0406943
{txt}{space 22}64  {c |}{col 27}{res}{space 2} .3835023{col 39}{space 2} .1210015{col 50}{space 1}   -3.04{col 59}{space 3}0.002{col 67}{space 4} .2066328{col 80}{space 3}  .711765
{txt}{space 22}65  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}66  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}67  {c |}{col 27}{res}{space 2} .0591918{col 39}{space 2}  .003935{col 50}{space 1}  -42.52{col 59}{space 3}0.000{col 67}{space 4} .0519606{col 80}{space 3} .0674292
{txt}{space 22}69  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}70  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}71  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}74  {c |}{col 27}{res}{space 2} .6669358{col 39}{space 2} .2122656{col 50}{space 1}   -1.27{col 59}{space 3}0.203{col 67}{space 4} .3574145{col 80}{space 3} 1.244503
{txt}{space 22}76  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}78  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}79  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}82  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}84  {c |}{col 27}{res}{space 2} .0047778{col 39}{space 2} .0025137{col 50}{space 1}  -10.16{col 59}{space 3}0.000{col 67}{space 4} .0017037{col 80}{space 3} .0133985
{txt}{space 22}86  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}88  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}89  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}92  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}93  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}96  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}98  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}101  {c |}{col 27}{res}{space 2} 2.085868{col 39}{space 2} 1.001336{col 50}{space 1}    1.53{col 59}{space 3}0.126{col 67}{space 4}   .81407{col 80}{space 3} 5.344561
{txt}{space 21}102  {c |}{col 27}{res}{space 2}  2.69796{col 39}{space 2} 1.437684{col 50}{space 1}    1.86{col 59}{space 3}0.063{col 67}{space 4} .9493993{col 80}{space 3} 7.666943
{txt}{space 21}104  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}105  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}106  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}107  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}108  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}109  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}110  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}111  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}112  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}113  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}114  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}115  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}116  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}117  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}118  {c |}{col 27}{res}{space 2} .1006672{col 39}{space 2} .0155969{col 50}{space 1}  -14.82{col 59}{space 3}0.000{col 67}{space 4} .0743032{col 80}{space 3} .1363857
{txt}{space 21}119  {c |}{col 27}{res}{space 2} .2322392{col 39}{space 2} .0111486{col 50}{space 1}  -30.41{col 59}{space 3}0.000{col 67}{space 4} .2113849{col 80}{space 3}  .255151
{txt}{space 21}120  {c |}{col 27}{res}{space 2} .1132602{col 39}{space 2} .0130729{col 50}{space 1}  -18.87{col 59}{space 3}0.000{col 67}{space 4} .0903293{col 80}{space 3} .1420124
{txt}{space 21}121  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}122  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}123  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}124  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}125  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}126  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}127  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}128  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}129  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}130  {c |}{col 27}{res}{space 2} .0728138{col 39}{space 2} .0078439{col 50}{space 1}  -24.32{col 59}{space 3}0.000{col 67}{space 4} .0589546{col 80}{space 3} .0899309
{txt}{space 21}131  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}133  {c |}{col 27}{res}{space 2}  3.51996{col 39}{space 2}  1.81149{col 50}{space 1}    2.45{col 59}{space 3}0.014{col 67}{space 4} 1.283751{col 80}{space 3} 9.651496
{txt}{space 21}134  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}135  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}136  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}137  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}138  {c |}{col 27}{res}{space 2} .0674592{col 39}{space 2} .0140869{col 50}{space 1}  -12.91{col 59}{space 3}0.000{col 67}{space 4} .0448015{col 80}{space 3} .1015757
{txt}{space 21}139  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}140  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}141  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}142  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}143  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}144  {c |}{col 27}{res}{space 2} .1286168{col 39}{space 2} .0130978{col 50}{space 1}  -20.14{col 59}{space 3}0.000{col 67}{space 4} .1053452{col 80}{space 3} .1570294
{txt}{space 21}145  {c |}{col 27}{res}{space 2} .0917791{col 39}{space 2} .0094727{col 50}{space 1}  -23.14{col 59}{space 3}0.000{col 67}{space 4} .0749705{col 80}{space 3} .1123564
{txt}{space 21}146  {c |}{col 27}{res}{space 2} .1649241{col 39}{space 2} .0119449{col 50}{space 1}  -24.88{col 59}{space 3}0.000{col 67}{space 4} .1430984{col 80}{space 3} .1900788
{txt}{space 21}147  {c |}{col 27}{res}{space 2} .1309674{col 39}{space 2} .0256441{col 50}{space 1}  -10.38{col 59}{space 3}0.000{col 67}{space 4} .0892265{col 80}{space 3} .1922351
{txt}{space 21}149  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}150  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}151  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}152  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}156  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}157  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}164  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}168  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}169  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}170  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}171  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} 6.734043{col 39}{space 2} 1.257992{col 50}{space 1}   10.21{col 59}{space 3}0.000{col 67}{space 4} 4.669412{col 80}{space 3} 9.711573
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                    {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 1.83e-34{col 39}{space 2} 2.64e-34{col 67}{space 4} 1.09e-35{col 80}{space 3} 3.08e-33
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M2
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}15
{txt}
{com}. 
. melogit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd i.country_elec if p_radicalrl_vs_mainstream==0 ///
> || ccode:, or vce(robust)
{res}{txt}note: {bf:5.country_elec} != 0 predicts failure perfectly;
      {bf:5.country_elec} omitted and 134 obs not used.

note: {bf:22.country_elec} != 0 predicts failure perfectly;
      {bf:22.country_elec} omitted and 379 obs not used.

note: {bf:30.country_elec} != 0 predicts failure perfectly;
      {bf:30.country_elec} omitted and 833 obs not used.

note: {bf:32.country_elec} != 0 predicts failure perfectly;
      {bf:32.country_elec} omitted and 822 obs not used.

note: {bf:34.country_elec} != 0 predicts failure perfectly;
      {bf:34.country_elec} omitted and 377 obs not used.

note: {bf:35.country_elec} != 0 predicts failure perfectly;
      {bf:35.country_elec} omitted and 975 obs not used.

note: {bf:168.country_elec} != 0 predicts failure perfectly;
      {bf:168.country_elec} omitted and 618 obs not used.

note: {bf:169.country_elec} != 0 predicts failure perfectly;
      {bf:169.country_elec} omitted and 399 obs not used.

note: {bf:171.country_elec} != 0 predicts failure perfectly;
      {bf:171.country_elec} omitted and 423 obs not used.

note: {bf:7.country_elec} identifies no observations in the sample.
note: {bf:27.country_elec} identifies no observations in the sample.
note: {bf:28.country_elec} identifies no observations in the sample.
note: {bf:29.country_elec} identifies no observations in the sample.
note: {bf:31.country_elec} identifies no observations in the sample.
note: {bf:33.country_elec} identifies no observations in the sample.
note: {bf:40.country_elec} identifies no observations in the sample.
note: {bf:44.country_elec} identifies no observations in the sample.
note: {bf:53.country_elec} identifies no observations in the sample.
note: {bf:54.country_elec} identifies no observations in the sample.
note: {bf:55.country_elec} identifies no observations in the sample.
note: {bf:56.country_elec} identifies no observations in the sample.
note: {bf:57.country_elec} identifies no observations in the sample.
note: {bf:58.country_elec} identifies no observations in the sample.
note: {bf:59.country_elec} identifies no observations in the sample.
note: {bf:60.country_elec} identifies no observations in the sample.
note: {bf:61.country_elec} identifies no observations in the sample.
note: {bf:69.country_elec} identifies no observations in the sample.
note: {bf:70.country_elec} identifies no observations in the sample.
note: {bf:71.country_elec} identifies no observations in the sample.
note: {bf:76.country_elec} identifies no observations in the sample.
note: {bf:78.country_elec} identifies no observations in the sample.
note: {bf:79.country_elec} identifies no observations in the sample.
note: {bf:82.country_elec} identifies no observations in the sample.
note: {bf:86.country_elec} identifies no observations in the sample.
note: {bf:88.country_elec} identifies no observations in the sample.
note: {bf:89.country_elec} identifies no observations in the sample.
note: {bf:92.country_elec} identifies no observations in the sample.
note: {bf:93.country_elec} identifies no observations in the sample.
note: {bf:96.country_elec} identifies no observations in the sample.
note: {bf:98.country_elec} identifies no observations in the sample.
note: {bf:104.country_elec} identifies no observations in the sample.
note: {bf:105.country_elec} identifies no observations in the sample.
note: {bf:106.country_elec} identifies no observations in the sample.
note: {bf:107.country_elec} identifies no observations in the sample.
note: {bf:108.country_elec} identifies no observations in the sample.
note: {bf:109.country_elec} identifies no observations in the sample.
note: {bf:110.country_elec} identifies no observations in the sample.
note: {bf:111.country_elec} identifies no observations in the sample.
note: {bf:112.country_elec} identifies no observations in the sample.
note: {bf:113.country_elec} identifies no observations in the sample.
note: {bf:114.country_elec} identifies no observations in the sample.
note: {bf:115.country_elec} identifies no observations in the sample.
note: {bf:116.country_elec} identifies no observations in the sample.
note: {bf:117.country_elec} identifies no observations in the sample.
note: {bf:121.country_elec} identifies no observations in the sample.
note: {bf:122.country_elec} identifies no observations in the sample.
note: {bf:123.country_elec} identifies no observations in the sample.
note: {bf:124.country_elec} identifies no observations in the sample.
note: {bf:125.country_elec} identifies no observations in the sample.
note: {bf:126.country_elec} identifies no observations in the sample.
note: {bf:127.country_elec} identifies no observations in the sample.
note: {bf:128.country_elec} identifies no observations in the sample.
note: {bf:129.country_elec} identifies no observations in the sample.
note: {bf:131.country_elec} identifies no observations in the sample.
note: {bf:134.country_elec} identifies no observations in the sample.
note: {bf:135.country_elec} identifies no observations in the sample.
note: {bf:136.country_elec} identifies no observations in the sample.
note: {bf:137.country_elec} identifies no observations in the sample.
note: {bf:139.country_elec} identifies no observations in the sample.
note: {bf:140.country_elec} identifies no observations in the sample.
note: {bf:141.country_elec} identifies no observations in the sample.
note: {bf:142.country_elec} identifies no observations in the sample.
note: {bf:143.country_elec} identifies no observations in the sample.
note: {bf:144.country_elec} omitted because of collinearity.
note: {bf:145.country_elec} omitted because of collinearity.
note: {bf:146.country_elec} omitted because of collinearity.
note: {bf:147.country_elec} omitted because of collinearity.
note: {bf:149.country_elec} identifies no observations in the sample.
note: {bf:150.country_elec} identifies no observations in the sample.
note: {bf:151.country_elec} identifies no observations in the sample.
note: {bf:152.country_elec} identifies no observations in the sample.
note: {bf:155.country_elec} identifies no observations in the sample.
note: {bf:156.country_elec} identifies no observations in the sample.
note: {bf:157.country_elec} identifies no observations in the sample.
note: {bf:164.country_elec} identifies no observations in the sample.
note: {bf:170.country_elec} identifies no observations in the sample.

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-4861.2954}  
Iteration 1:{space 3}log likelihood = {res:-4338.2072}  
Iteration 2:{space 3}log likelihood = {res: -4331.023}  
Iteration 3:{space 3}log likelihood = {res:-4330.9859}  
Iteration 4:{space 3}log likelihood = {res:-4330.9859}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -4357.087}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -4357.087}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res: -4345.068}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-4341.4847}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-4337.5539}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-4332.7832}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-4331.7061}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res:-4331.2501}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-4331.0631}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-4330.9876}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res:-4330.9871}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 22:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 23:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 25:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 27:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 30:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 32:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 35:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 37:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 40:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 41:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 43:{space 2}log pseudolikelihood = {res: -4330.987}  (backed up)
Iteration 44:{space 2}log pseudolikelihood = {res: -4330.987}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res: -4330.987}  
Iteration 46:{space 2}log pseudolikelihood = {res:-4330.9869}  (not concave)
Iteration 47:{space 2}log pseudolikelihood = {res:-4330.9869}  
Iteration 48:{space 2}log pseudolikelihood = {res:-4330.9867}  
Iteration 49:{space 2}log pseudolikelihood = {res:-4330.9859}  
Iteration 50:{space 2}log pseudolikelihood = {res:-4330.9859}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    20,082
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        13

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        78
{col 63}{txt}avg{col 67}={res}{col 69}   1,544.8
{col 63}{txt}max{col 67}={res}{col 69}     4,330

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-4330.9859{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 92:(Std. err. adjusted for {res:13} clusters in {res:ccode})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_radicalrl_vs_mainstream{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} 1.314152{col 40}{space 2} .0954517{col 51}{space 1}    3.76{col 60}{space 3}0.000{col 68}{space 4} 1.139776{col 81}{space 3} 1.515205
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9874794{col 40}{space 2} .0030771{col 51}{space 1}   -4.04{col 60}{space 3}0.000{col 68}{space 4} .9814668{col 81}{space 3} .9935289
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.345012{col 40}{space 2}  .186458{col 51}{space 1}    2.14{col 60}{space 3}0.033{col 68}{space 4} 1.025002{col 81}{space 3} 1.764931
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} 1.135743{col 40}{space 2} .2288274{col 51}{space 1}    0.63{col 60}{space 3}0.528{col 68}{space 4} .7652116{col 81}{space 3} 1.685693
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} 2.698913{col 40}{space 2} .3513927{col 51}{space 1}    7.63{col 60}{space 3}0.000{col 68}{space 4}  2.09105{col 81}{space 3} 3.483482
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} .7415547{col 40}{space 2} .0673758{col 51}{space 1}   -3.29{col 60}{space 3}0.001{col 68}{space 4} .6205905{col 81}{space 3}  .886097
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} .5481165{col 40}{space 2} .0562836{col 51}{space 1}   -5.86{col 60}{space 3}0.000{col 68}{space 4} .4481948{col 81}{space 3}  .670315
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.037848{col 40}{space 2} .0470251{col 51}{space 1}    0.82{col 60}{space 3}0.412{col 68}{space 4} .9496542{col 81}{space 3} 1.134232
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3506769{col 40}{space 2} .0652358{col 51}{space 1}   -5.63{col 60}{space 3}0.000{col 68}{space 4}  .243534{col 81}{space 3} .5049574
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.158517{col 40}{space 2} .2064977{col 51}{space 1}    0.83{col 60}{space 3}0.409{col 68}{space 4} .8169242{col 81}{space 3} 1.642946
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} .0489718{col 40}{space 2} .0068884{col 51}{space 1}  -21.45{col 60}{space 3}0.000{col 68}{space 4} .0371721{col 81}{space 3} .0645173
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} 424.3628{col 40}{space 2} 118.7034{col 51}{space 1}   21.63{col 60}{space 3}0.000{col 68}{space 4} 245.2663{col 81}{space 3} 734.2378
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} 1.58e-34{col 40}{space 2} 5.67e-34{col 51}{space 1}  -21.61{col 60}{space 3}0.000{col 68}{space 4} 1.36e-37{col 81}{space 3} 1.83e-31
{txt}{space 26} {c |}
{space 14}country_elec {c |}
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{space 22}155  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}156  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}157  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}164  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}168  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}169  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}170  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}171  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} 6.28e-12{col 40}{space 2} 8.09e-12{col 51}{space 1}  -20.03{col 60}{space 3}0.000{col 68}{space 4} 5.04e-13{col 81}{space 3} 7.84e-11
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                     {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 8.67e-34{col 40}{space 2} 5.91e-33{col 68}{space 4} 1.36e-39{col 81}{space 3} 5.53e-28
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M3
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}13
{txt}
{com}. 
. melogit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lpss_mod3_upd i.country_elec if p_radicalrl_vs_mainstream==1 ///
> || ccode:, or vce(robust)
{res}{txt}note: {bf:25.country_elec} != 0 predicts success perfectly;
      {bf:25.country_elec} omitted and 8 obs not used.

note: {bf:44.country_elec} identifies no observations in the sample.
note: {bf:53.country_elec} identifies no observations in the sample.
note: {bf:64.country_elec} identifies no observations in the sample.
note: {bf:65.country_elec} identifies no observations in the sample.
note: {bf:66.country_elec} identifies no observations in the sample.
note: {bf:76.country_elec} identifies no observations in the sample.
note: {bf:78.country_elec} identifies no observations in the sample.
note: {bf:79.country_elec} identifies no observations in the sample.
note: {bf:82.country_elec} identifies no observations in the sample.
note: {bf:86.country_elec} identifies no observations in the sample.
note: {bf:88.country_elec} identifies no observations in the sample.
note: {bf:89.country_elec} identifies no observations in the sample.
note: {bf:92.country_elec} identifies no observations in the sample.
note: {bf:96.country_elec} identifies no observations in the sample.
note: {bf:104.country_elec} identifies no observations in the sample.
note: {bf:105.country_elec} identifies no observations in the sample.
note: {bf:106.country_elec} identifies no observations in the sample.
note: {bf:107.country_elec} identifies no observations in the sample.
note: {bf:108.country_elec} identifies no observations in the sample.
note: {bf:109.country_elec} identifies no observations in the sample.
note: {bf:110.country_elec} identifies no observations in the sample.
note: {bf:111.country_elec} identifies no observations in the sample.
note: {bf:112.country_elec} identifies no observations in the sample.
note: {bf:113.country_elec} identifies no observations in the sample.
note: {bf:114.country_elec} identifies no observations in the sample.
note: {bf:115.country_elec} identifies no observations in the sample.
note: {bf:116.country_elec} identifies no observations in the sample.
note: {bf:117.country_elec} identifies no observations in the sample.
note: {bf:121.country_elec} identifies no observations in the sample.
note: {bf:122.country_elec} identifies no observations in the sample.
note: {bf:123.country_elec} identifies no observations in the sample.
note: {bf:124.country_elec} identifies no observations in the sample.
note: {bf:125.country_elec} identifies no observations in the sample.
note: {bf:126.country_elec} identifies no observations in the sample.
note: {bf:127.country_elec} identifies no observations in the sample.
note: {bf:128.country_elec} identifies no observations in the sample.
note: {bf:129.country_elec} identifies no observations in the sample.
note: {bf:131.country_elec} identifies no observations in the sample.
note: {bf:134.country_elec} identifies no observations in the sample.
note: {bf:135.country_elec} identifies no observations in the sample.
note: {bf:136.country_elec} identifies no observations in the sample.
note: {bf:137.country_elec} identifies no observations in the sample.
note: {bf:139.country_elec} identifies no observations in the sample.
note: {bf:140.country_elec} identifies no observations in the sample.
note: {bf:141.country_elec} identifies no observations in the sample.
note: {bf:142.country_elec} identifies no observations in the sample.
note: {bf:143.country_elec} identifies no observations in the sample.
note: {bf:145.country_elec} omitted because of collinearity.
note: {bf:146.country_elec} omitted because of collinearity.
note: {bf:147.country_elec} omitted because of collinearity.
note: {bf:156.country_elec} identifies no observations in the sample.
note: {bf:157.country_elec} identifies no observations in the sample.

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1257.5853}  
Iteration 1:{space 3}log likelihood = {res:-1246.5813}  
Iteration 2:{space 3}log likelihood = {res:-1246.4617}  
Iteration 3:{space 3}log likelihood = {res:-1246.4615}  
Iteration 4:{space 3}log likelihood = {res:-1246.4615}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1265.2303}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1265.2303}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1260.8765}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-1256.5012}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-1251.1927}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-1248.3975}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-1247.0685}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res:-1246.4836}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-1246.4689}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res: -1246.463}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res:-1246.4624}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res:-1246.4623}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 23:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 25:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 28:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 29:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 30:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 31:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 33:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 36:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 37:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 40:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 42:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 43:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 46:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 47:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 48:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 50:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 51:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 52:{space 2}log pseudolikelihood = {res:-1246.4622}  (backed up)
Iteration 53:{space 2}log pseudolikelihood = {res:-1246.4622}  (not concave)
Iteration 54:{space 2}log pseudolikelihood = {res:-1246.4622}  
Iteration 55:{space 2}log pseudolikelihood = {res: -1246.462}  
Iteration 56:{space 2}log pseudolikelihood = {res:-1246.4619}  
Iteration 57:{space 2}log pseudolikelihood = {res:-1246.4616}  
Iteration 58:{space 2}log pseudolikelihood = {res:-1246.4615}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     2,708
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        13

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        19
{col 63}{txt}avg{col 67}={res}{col 69}     208.3
{col 63}{txt}max{col 67}={res}{col 69}       798

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-1246.4615{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 92:(Std. err. adjusted for {res:13} clusters in {res:ccode})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1} c_mainstream_vs_radicalrl{col 28}{c |} Odds ratio{col 40}   std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}male {c |}{col 28}{res}{space 2} .8933236{col 40}{space 2} .1429576{col 51}{space 1}   -0.70{col 60}{space 3}0.481{col 68}{space 4} .6528177{col 81}{space 3} 1.222435
{txt}{space 23}age {c |}{col 28}{res}{space 2} .9926899{col 40}{space 2} .0040801{col 51}{space 1}   -1.79{col 60}{space 3}0.074{col 68}{space 4} .9847251{col 81}{space 3} 1.000719
{txt}{space 26} {c |}
{space 19}highedu {c |}
{space 6}Secondary education  {c |}{col 28}{res}{space 2} 1.175129{col 40}{space 2} .1848009{col 51}{space 1}    1.03{col 60}{space 3}0.305{col 68}{space 4} .8634269{col 81}{space 3} 1.599358
{txt}{space 1}Post-secondary education  {c |}{col 28}{res}{space 2} .7368528{col 40}{space 2} .1425377{col 51}{space 1}   -1.58{col 60}{space 3}0.114{col 68}{space 4} .5043398{col 81}{space 3}  1.07656
{txt}{space 26} {c |}
{space 14}dissatisfied {c |}{col 28}{res}{space 2} .7017177{col 40}{space 2} .0490872{col 51}{space 1}   -5.06{col 60}{space 3}0.000{col 68}{space 4} .6118127{col 81}{space 3} .8048342
{txt}{space 26} {c |}
{space 15}income_3cat {c |}
{space 12}Medium income  {c |}{col 28}{res}{space 2} 1.103518{col 40}{space 2} .1393677{col 51}{space 1}    0.78{col 60}{space 3}0.435{col 68}{space 4}  .861545{col 81}{space 3} 1.413453
{txt}{space 14}High income  {c |}{col 28}{res}{space 2} 1.779752{col 40}{space 2} .2544692{col 51}{space 1}    4.03{col 60}{space 3}0.000{col 68}{space 4} 1.344791{col 81}{space 3} 2.355399
{txt}{space 26} {c |}
{space 6}distpreviouspartycmp {c |}{col 28}{res}{space 2} 1.051276{col 40}{space 2} .0488826{col 51}{space 1}    1.08{col 60}{space 3}0.282{col 68}{space 4} .9597044{col 81}{space 3} 1.151586
{txt}{space 16}closeparty {c |}{col 28}{res}{space 2} .3920988{col 40}{space 2}  .043144{col 51}{space 1}   -8.51{col 60}{space 3}0.000{col 68}{space 4} .3160347{col 81}{space 3} .4864703
{txt}{space 14}p_government {c |}{col 28}{res}{space 2} 1.479783{col 40}{space 2} .4708706{col 51}{space 1}    1.23{col 60}{space 3}0.218{col 68}{space 4} .7931288{col 81}{space 3} 2.760911
{txt}{space 19}sd_rile {c |}{col 28}{res}{space 2} 1.271288{col 40}{space 2} .0143965{col 51}{space 1}   21.20{col 60}{space 3}0.000{col 68}{space 4} 1.243383{col 81}{space 3}  1.29982
{txt}lvotetotradicallr_combined {c |}{col 28}{res}{space 2} .8442924{col 40}{space 2}  .006301{col 51}{space 1}  -22.68{col 60}{space 3}0.000{col 68}{space 4} .8320325{col 81}{space 3}  .856733
{txt}{space 13}lpss_mod3_upd {c |}{col 28}{res}{space 2} 76.62239{col 40}{space 2}  17.9807{col 51}{space 1}   18.49{col 60}{space 3}0.000{col 68}{space 4} 48.37347{col 81}{space 3}  121.368
{txt}{space 26} {c |}
{space 14}country_elec {c |}
{space 23}24  {c |}{col 28}{res}{space 2} 5.306392{col 40}{space 2} 1.125657{col 51}{space 1}    7.87{col 60}{space 3}0.000{col 68}{space 4} 3.501322{col 81}{space 3} 8.042047
{txt}{space 23}25  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}26  {c |}{col 28}{res}{space 2}  3.84136{col 40}{space 2} .7798542{col 51}{space 1}    6.63{col 60}{space 3}0.000{col 68}{space 4} 2.580348{col 81}{space 3} 5.718627
{txt}{space 23}36  {c |}{col 28}{res}{space 2} 51.00997{col 40}{space 2} 16.56708{col 51}{space 1}   12.11{col 60}{space 3}0.000{col 68}{space 4} 26.98987{col 81}{space 3} 96.40718
{txt}{space 23}37  {c |}{col 28}{res}{space 2} 67.83915{col 40}{space 2} 20.80685{col 51}{space 1}   13.75{col 60}{space 3}0.000{col 68}{space 4} 37.18858{col 81}{space 3} 123.7517
{txt}{space 23}43  {c |}{col 28}{res}{space 2} 1.519463{col 40}{space 2} .5779085{col 51}{space 1}    1.10{col 60}{space 3}0.271{col 68}{space 4} .7210188{col 81}{space 3} 3.202093
{txt}{space 23}44  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}45  {c |}{col 28}{res}{space 2} 8.343348{col 40}{space 2} 2.887868{col 51}{space 1}    6.13{col 60}{space 3}0.000{col 68}{space 4} 4.233656{col 81}{space 3}  16.4424
{txt}{space 23}48  {c |}{col 28}{res}{space 2} 80500.21{col 40}{space 2} 48182.08{col 51}{space 1}   18.87{col 60}{space 3}0.000{col 68}{space 4} 24907.17{col 81}{space 3} 260177.5
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 3.969388{col 40}{space 2} .5942532{col 51}{space 1}    9.21{col 60}{space 3}0.000{col 68}{space 4} 2.959995{col 81}{space 3} 5.322996
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 49.52315{col 40}{space 2} 12.12471{col 51}{space 1}   15.94{col 60}{space 3}0.000{col 68}{space 4} 30.64851{col 81}{space 3} 80.02159
{txt}{space 23}53  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}62  {c |}{col 28}{res}{space 2} .0902517{col 40}{space 2} .0068738{col 51}{space 1}  -31.58{col 60}{space 3}0.000{col 68}{space 4} .0777366{col 81}{space 3} .1047816
{txt}{space 23}64  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}65  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}66  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}67  {c |}{col 28}{res}{space 2} .0009758{col 40}{space 2} .0003218{col 51}{space 1}  -21.02{col 60}{space 3}0.000{col 68}{space 4} .0005113{col 81}{space 3} .0018623
{txt}{space 23}74  {c |}{col 28}{res}{space 2} .3650923{col 40}{space 2} .0701226{col 51}{space 1}   -5.25{col 60}{space 3}0.000{col 68}{space 4} .2505613{col 81}{space 3} .5319752
{txt}{space 23}76  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}78  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}79  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}82  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}84  {c |}{col 28}{res}{space 2} 6.00e-07{col 40}{space 2} 5.27e-07{col 51}{space 1}  -16.29{col 60}{space 3}0.000{col 68}{space 4} 1.07e-07{col 81}{space 3} 3.36e-06
{txt}{space 23}86  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}88  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}89  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}92  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 23}96  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}101  {c |}{col 28}{res}{space 2} .0220727{col 40}{space 2} .0097955{col 51}{space 1}   -8.59{col 60}{space 3}0.000{col 68}{space 4} .0092493{col 81}{space 3}  .052675
{txt}{space 22}102  {c |}{col 28}{res}{space 2} .0745095{col 40}{space 2}  .013867{col 51}{space 1}  -13.95{col 60}{space 3}0.000{col 68}{space 4} .0517362{col 81}{space 3} .1073073
{txt}{space 22}104  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}105  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}106  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}107  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}108  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}109  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}110  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}111  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}112  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}113  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}114  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}115  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}116  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}117  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}118  {c |}{col 28}{res}{space 2} .2293988{col 40}{space 2} .0290494{col 51}{space 1}  -11.63{col 60}{space 3}0.000{col 68}{space 4} .1789786{col 81}{space 3}  .294023
{txt}{space 22}119  {c |}{col 28}{res}{space 2} 4.151656{col 40}{space 2} .5398072{col 51}{space 1}   10.95{col 60}{space 3}0.000{col 68}{space 4} 3.217706{col 81}{space 3} 5.356689
{txt}{space 22}120  {c |}{col 28}{res}{space 2} .0254419{col 40}{space 2}  .003477{col 51}{space 1}  -26.86{col 60}{space 3}0.000{col 68}{space 4} .0194635{col 81}{space 3} .0332565
{txt}{space 22}121  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}122  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}123  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}124  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}125  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}126  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}127  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}128  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}129  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}130  {c |}{col 28}{res}{space 2} .0632258{col 40}{space 2} .0095332{col 51}{space 1}  -18.31{col 60}{space 3}0.000{col 68}{space 4}  .047049{col 81}{space 3} .0849647
{txt}{space 22}131  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}133  {c |}{col 28}{res}{space 2} 184.0993{col 40}{space 2} 40.18486{col 51}{space 1}   23.89{col 60}{space 3}0.000{col 68}{space 4} 120.0199{col 81}{space 3}  282.391
{txt}{space 22}134  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}135  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}136  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}137  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}138  {c |}{col 28}{res}{space 2} .0071624{col 40}{space 2} .0022833{col 51}{space 1}  -15.49{col 60}{space 3}0.000{col 68}{space 4} .0038345{col 81}{space 3} .0133787
{txt}{space 22}139  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}140  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}141  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}142  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}143  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}144  {c |}{col 28}{res}{space 2}  .751023{col 40}{space 2} .0510305{col 51}{space 1}   -4.21{col 60}{space 3}0.000{col 68}{space 4}  .657379{col 81}{space 3} .8580066
{txt}{space 22}145  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 22}146  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 22}147  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 22}156  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 22}157  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (empty)
{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} .0260838{col 40}{space 2} .0089416{col 51}{space 1}  -10.64{col 60}{space 3}0.000{col 68}{space 4} .0133222{col 81}{space 3} .0510701
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                     {col 28}{txt}{c |}
{space 17}var(_cons){c |}{col 28}{res}{space 2} 2.52e-34{col 40}{space 2} 2.32e-34{col 68}{space 4} 4.14e-35{col 81}{space 3} 1.54e-33
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M4
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}13
{txt}
{com}. 
. melogit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd i.country_elec if p_green_vs_mainstream==0 ///
> || ccode:, or vce(robust)
{res}{txt}note: {bf:5.country_elec} != 0 predicts failure perfectly;
      {bf:5.country_elec} omitted and 134 obs not used.

note: {bf:22.country_elec} != 0 predicts failure perfectly;
      {bf:22.country_elec} omitted and 379 obs not used.

note: {bf:30.country_elec} != 0 predicts failure perfectly;
      {bf:30.country_elec} omitted and 833 obs not used.

note: {bf:32.country_elec} != 0 predicts failure perfectly;
      {bf:32.country_elec} omitted and 822 obs not used.

note: {bf:34.country_elec} != 0 predicts failure perfectly;
      {bf:34.country_elec} omitted and 377 obs not used.

note: {bf:35.country_elec} != 0 predicts failure perfectly;
      {bf:35.country_elec} omitted and 975 obs not used.

note: {bf:48.country_elec} != 0 predicts failure perfectly;
      {bf:48.country_elec} omitted and 374 obs not used.

note: {bf:50.country_elec} != 0 predicts failure perfectly;
      {bf:50.country_elec} omitted and 372 obs not used.

note: {bf:51.country_elec} != 0 predicts failure perfectly;
      {bf:51.country_elec} omitted and 814 obs not used.

note: {bf:130.country_elec} != 0 predicts failure perfectly;
      {bf:130.country_elec} omitted and 820 obs not used.

note: {bf:133.country_elec} != 0 predicts failure perfectly;
      {bf:133.country_elec} omitted and 767 obs not used.

note: {bf:138.country_elec} != 0 predicts failure perfectly;
      {bf:138.country_elec} omitted and 451 obs not used.

note: {bf:144.country_elec} != 0 predicts failure perfectly;
      {bf:144.country_elec} omitted and 1032 obs not used.

note: {bf:145.country_elec} != 0 predicts failure perfectly;
      {bf:145.country_elec} omitted and 1035 obs not used.

note: {bf:146.country_elec} != 0 predicts failure perfectly;
      {bf:146.country_elec} omitted and 1080 obs not used.

note: {bf:147.country_elec} != 0 predicts failure perfectly;
      {bf:147.country_elec} omitted and 450 obs not used.

note: {bf:7.country_elec} identifies no observations in the sample.
note: {bf:27.country_elec} identifies no observations in the sample.
note: {bf:28.country_elec} identifies no observations in the sample.
note: {bf:29.country_elec} identifies no observations in the sample.
note: {bf:31.country_elec} identifies no observations in the sample.
note: {bf:33.country_elec} identifies no observations in the sample.
note: {bf:40.country_elec} identifies no observations in the sample.
note: {bf:44.country_elec} identifies no observations in the sample.
note: {bf:53.country_elec} identifies no observations in the sample.
note: {bf:54.country_elec} identifies no observations in the sample.
note: {bf:55.country_elec} identifies no observations in the sample.
note: {bf:56.country_elec} identifies no observations in the sample.
note: {bf:57.country_elec} identifies no observations in the sample.
note: {bf:58.country_elec} identifies no observations in the sample.
note: {bf:59.country_elec} identifies no observations in the sample.
note: {bf:60.country_elec} identifies no observations in the sample.
note: {bf:61.country_elec} identifies no observations in the sample.
note: {bf:69.country_elec} identifies no observations in the sample.
note: {bf:70.country_elec} identifies no observations in the sample.
note: {bf:71.country_elec} identifies no observations in the sample.
note: {bf:76.country_elec} identifies no observations in the sample.
note: {bf:78.country_elec} identifies no observations in the sample.
note: {bf:79.country_elec} identifies no observations in the sample.
note: {bf:82.country_elec} identifies no observations in the sample.
note: {bf:86.country_elec} identifies no observations in the sample.
note: {bf:88.country_elec} identifies no observations in the sample.
note: {bf:89.country_elec} identifies no observations in the sample.
note: {bf:92.country_elec} identifies no observations in the sample.
note: {bf:93.country_elec} identifies no observations in the sample.
note: {bf:96.country_elec} identifies no observations in the sample.
note: {bf:98.country_elec} identifies no observations in the sample.
note: {bf:104.country_elec} identifies no observations in the sample.
note: {bf:105.country_elec} identifies no observations in the sample.
note: {bf:106.country_elec} identifies no observations in the sample.
note: {bf:107.country_elec} identifies no observations in the sample.
note: {bf:108.country_elec} identifies no observations in the sample.
note: {bf:109.country_elec} identifies no observations in the sample.
note: {bf:110.country_elec} identifies no observations in the sample.
note: {bf:111.country_elec} identifies no observations in the sample.
note: {bf:112.country_elec} identifies no observations in the sample.
note: {bf:113.country_elec} identifies no observations in the sample.
note: {bf:114.country_elec} identifies no observations in the sample.
note: {bf:115.country_elec} identifies no observations in the sample.
note: {bf:116.country_elec} identifies no observations in the sample.
note: {bf:117.country_elec} identifies no observations in the sample.
note: {bf:120.country_elec} omitted because of collinearity.
note: {bf:121.country_elec} identifies no observations in the sample.
note: {bf:122.country_elec} identifies no observations in the sample.
note: {bf:123.country_elec} identifies no observations in the sample.
note: {bf:124.country_elec} identifies no observations in the sample.
note: {bf:125.country_elec} identifies no observations in the sample.
note: {bf:126.country_elec} identifies no observations in the sample.
note: {bf:127.country_elec} identifies no observations in the sample.
note: {bf:128.country_elec} identifies no observations in the sample.
note: {bf:129.country_elec} identifies no observations in the sample.
note: {bf:131.country_elec} identifies no observations in the sample.
note: {bf:134.country_elec} identifies no observations in the sample.
note: {bf:135.country_elec} identifies no observations in the sample.
note: {bf:136.country_elec} identifies no observations in the sample.
note: {bf:137.country_elec} identifies no observations in the sample.
note: {bf:139.country_elec} identifies no observations in the sample.
note: {bf:140.country_elec} identifies no observations in the sample.
note: {bf:141.country_elec} identifies no observations in the sample.
note: {bf:142.country_elec} identifies no observations in the sample.
note: {bf:143.country_elec} identifies no observations in the sample.
note: {bf:149.country_elec} identifies no observations in the sample.
note: {bf:150.country_elec} identifies no observations in the sample.
note: {bf:151.country_elec} identifies no observations in the sample.
note: {bf:152.country_elec} identifies no observations in the sample.
note: {bf:155.country_elec} identifies no observations in the sample.
note: {bf:156.country_elec} identifies no observations in the sample.
note: {bf:157.country_elec} identifies no observations in the sample.
note: {bf:164.country_elec} identifies no observations in the sample.
note: {bf:168.country_elec} omitted because of collinearity.
note: {bf:169.country_elec} omitted because of collinearity.
note: {bf:170.country_elec} identifies no observations in the sample.
note: {bf:171.country_elec} omitted because of collinearity.

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2607.8798}  
Iteration 1:{space 3}log likelihood = {res:-2193.1028}  
Iteration 2:{space 3}log likelihood = {res:-2181.1152}  
Iteration 3:{space 3}log likelihood = {res:-2180.9472}  
Iteration 4:{space 3}log likelihood = {res:-2180.9471}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2198.8691}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2198.8691}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2194.8396}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-2190.7733}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-2185.1487}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-2181.8921}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-2181.1705}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res:-2181.0229}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-2180.9633}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-2180.9513}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res:-2180.9489}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res:-2180.9487}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 20:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 21:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 22:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 23:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 26:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 28:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 29:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 32:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 35:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 36:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 37:{space 2}log pseudolikelihood = {res:-2180.9486}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 39:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 40:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 41:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res:-2180.9486}  
Iteration 43:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 45:{space 2}log pseudolikelihood = {res:-2180.9486}  (not concave)
Iteration 46:{space 2}log pseudolikelihood = {res:-2180.9486}  
Iteration 47:{space 2}log pseudolikelihood = {res:-2180.9475}  
Iteration 48:{space 2}log pseudolikelihood = {res:-2180.9473}  
Iteration 49:{space 2}log pseudolikelihood = {res:-2180.9471}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    13,560
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        10

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        70
{col 63}{txt}avg{col 67}={res}{col 69}   1,356.0
{col 63}{txt}max{col 67}={res}{col 69}     3,703

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-2180.9471{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 91:(Std. err. adjusted for {res:10} clusters in {res:ccode})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_green_vs_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2}  .681327{col 39}{space 2} .0422903{col 50}{space 1}   -6.18{col 59}{space 3}0.000{col 67}{space 4}  .603283{col 80}{space 3} .7694671
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9785751{col 39}{space 2} .0035952{col 50}{space 1}   -5.90{col 59}{space 3}0.000{col 67}{space 4}  .971554{col 80}{space 3} .9856469
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.695077{col 39}{space 2} .5232534{col 50}{space 1}    1.71{col 59}{space 3}0.087{col 67}{space 4} .9256188{col 80}{space 3}  3.10418
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 3.240825{col 39}{space 2} .9670839{col 50}{space 1}    3.94{col 59}{space 3}0.000{col 67}{space 4} 1.805724{col 80}{space 3} 5.816474
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.146299{col 39}{space 2} .1419114{col 50}{space 1}    1.10{col 59}{space 3}0.270{col 67}{space 4} .8993306{col 80}{space 3} 1.461088
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.040464{col 39}{space 2} .1410982{col 50}{space 1}    0.29{col 59}{space 3}0.770{col 67}{space 4} .7976184{col 80}{space 3} 1.357248
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .9534251{col 39}{space 2} .2091638{col 50}{space 1}   -0.22{col 59}{space 3}0.828{col 67}{space 4} .6202243{col 80}{space 3}  1.46563
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2}  .912188{col 39}{space 2}  .034647{col 50}{space 1}   -2.42{col 59}{space 3}0.016{col 67}{space 4} .8467471{col 80}{space 3} .9826865
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5365831{col 39}{space 2} .0458918{col 50}{space 1}   -7.28{col 59}{space 3}0.000{col 67}{space 4} .4537715{col 80}{space 3} .6345075
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} 1.502556{col 39}{space 2} .3492357{col 50}{space 1}    1.75{col 59}{space 3}0.080{col 67}{space 4} .9527692{col 80}{space 3} 2.369591
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.026747{col 39}{space 2} .0064682{col 50}{space 1}    4.19{col 59}{space 3}0.000{col 67}{space 4} 1.014147{col 80}{space 3} 1.039503
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} 1.027584{col 39}{space 2} .0137158{col 50}{space 1}    2.04{col 59}{space 3}0.041{col 67}{space 4}  1.00105{col 80}{space 3} 1.054821
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .9469397{col 39}{space 2} .0184179{col 50}{space 1}   -2.80{col 59}{space 3}0.005{col 67}{space 4} .9115206{col 80}{space 3} .9837351
{txt}{space 25} {c |}
{space 13}country_elec {c |}
{space 23}5  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 23}7  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}22  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}23  {c |}{col 27}{res}{space 2} 1.230148{col 39}{space 2} .0611411{col 50}{space 1}    4.17{col 59}{space 3}0.000{col 67}{space 4} 1.115965{col 80}{space 3} 1.356013
{txt}{space 22}24  {c |}{col 27}{res}{space 2} 1.431482{col 39}{space 2} .1575828{col 50}{space 1}    3.26{col 59}{space 3}0.001{col 67}{space 4} 1.153672{col 80}{space 3} 1.776189
{txt}{space 22}25  {c |}{col 27}{res}{space 2} 2.861044{col 39}{space 2} .3553399{col 50}{space 1}    8.46{col 59}{space 3}0.000{col 67}{space 4} 2.242879{col 80}{space 3} 3.649583
{txt}{space 22}26  {c |}{col 27}{res}{space 2}  .502302{col 39}{space 2} .0356323{col 50}{space 1}   -9.71{col 59}{space 3}0.000{col 67}{space 4} .4371015{col 80}{space 3} .5772281
{txt}{space 22}27  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}28  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}29  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}30  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}31  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}32  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}33  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}34  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}35  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}36  {c |}{col 27}{res}{space 2} 1.785084{col 39}{space 2} .1530574{col 50}{space 1}    6.76{col 59}{space 3}0.000{col 67}{space 4} 1.508949{col 80}{space 3} 2.111751
{txt}{space 22}37  {c |}{col 27}{res}{space 2} .6620977{col 39}{space 2} .0496381{col 50}{space 1}   -5.50{col 59}{space 3}0.000{col 67}{space 4}  .571619{col 80}{space 3} .7668978
{txt}{space 22}40  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}43  {c |}{col 27}{res}{space 2} .8687921{col 39}{space 2}  .067172{col 50}{space 1}   -1.82{col 59}{space 3}0.069{col 67}{space 4} .7466273{col 80}{space 3} 1.010946
{txt}{space 22}44  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}45  {c |}{col 27}{res}{space 2} 1.509536{col 39}{space 2} .1125281{col 50}{space 1}    5.52{col 59}{space 3}0.000{col 67}{space 4}  1.30434{col 80}{space 3} 1.747013
{txt}{space 22}48  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}50  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}51  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}53  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}54  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}55  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}56  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}57  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}58  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}59  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}60  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}61  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}62  {c |}{col 27}{res}{space 2} 1.312782{col 39}{space 2} .1336025{col 50}{space 1}    2.67{col 59}{space 3}0.007{col 67}{space 4} 1.075388{col 80}{space 3}  1.60258
{txt}{space 22}64  {c |}{col 27}{res}{space 2} 2.082006{col 39}{space 2} .1508664{col 50}{space 1}   10.12{col 59}{space 3}0.000{col 67}{space 4} 1.806352{col 80}{space 3} 2.399727
{txt}{space 22}65  {c |}{col 27}{res}{space 2} 1.496901{col 39}{space 2} .1546128{col 50}{space 1}    3.91{col 59}{space 3}0.000{col 67}{space 4}  1.22257{col 80}{space 3} 1.832789
{txt}{space 22}66  {c |}{col 27}{res}{space 2} 1.317479{col 39}{space 2} .1260844{col 50}{space 1}    2.88{col 59}{space 3}0.004{col 67}{space 4} 1.092151{col 80}{space 3} 1.589296
{txt}{space 22}67  {c |}{col 27}{res}{space 2} 2.892066{col 39}{space 2} .6129644{col 50}{space 1}    5.01{col 59}{space 3}0.000{col 67}{space 4} 1.908968{col 80}{space 3} 4.381449
{txt}{space 22}69  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}70  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}71  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}74  {c |}{col 27}{res}{space 2} 2.357568{col 39}{space 2}   .21286{col 50}{space 1}    9.50{col 59}{space 3}0.000{col 67}{space 4}   1.9752{col 80}{space 3} 2.813957
{txt}{space 22}76  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}78  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}79  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}82  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}84  {c |}{col 27}{res}{space 2} .7303201{col 39}{space 2} .1229409{col 50}{space 1}   -1.87{col 59}{space 3}0.062{col 67}{space 4} .5250774{col 80}{space 3} 1.015788
{txt}{space 22}86  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}88  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}89  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}92  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}93  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}96  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}98  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}101  {c |}{col 27}{res}{space 2} .6055138{col 39}{space 2} .0278872{col 50}{space 1}  -10.89{col 59}{space 3}0.000{col 67}{space 4} .5532501{col 80}{space 3} .6627146
{txt}{space 21}102  {c |}{col 27}{res}{space 2} 1.941852{col 39}{space 2} .1914949{col 50}{space 1}    6.73{col 59}{space 3}0.000{col 67}{space 4} 1.600572{col 80}{space 3} 2.355901
{txt}{space 21}104  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}105  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}106  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}107  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}108  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}109  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}110  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}111  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}112  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}113  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}114  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}115  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}116  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}117  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}118  {c |}{col 27}{res}{space 2} .6648583{col 39}{space 2} .0385807{col 50}{space 1}   -7.03{col 59}{space 3}0.000{col 67}{space 4} .5933831{col 80}{space 3}  .744943
{txt}{space 21}119  {c |}{col 27}{res}{space 2} .9381123{col 39}{space 2} .0281963{col 50}{space 1}   -2.13{col 59}{space 3}0.034{col 67}{space 4} .8844448{col 80}{space 3} .9950363
{txt}{space 21}120  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}121  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}122  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}123  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}124  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}125  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}126  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}127  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}128  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}129  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}130  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}131  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}133  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}134  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}135  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}136  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}137  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}138  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}139  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}140  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}141  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}142  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}143  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}144  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}145  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}146  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}147  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}149  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}150  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}151  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}152  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}155  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}156  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}157  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}164  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}168  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}169  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}170  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}171  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2} .0304295{col 39}{space 2} .0056216{col 50}{space 1}  -18.90{col 59}{space 3}0.000{col 67}{space 4} .0211856{col 80}{space 3} .0437067
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                    {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 2.07e-33{col 39}{space 2} 3.12e-34{col 67}{space 4} 1.54e-33{col 80}{space 3} 2.79e-33
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M5
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}10
{txt}
{com}. 
. melogit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lpss_mod3_upd i.country_elec if p_green_vs_mainstream==1 ///
> || ccode:, or vce(robust)
{res}{txt}note: {bf:25.country_elec} != 0 predicts success perfectly;
      {bf:25.country_elec} omitted and 6 obs not used.

note: {bf:7.country_elec} identifies no observations in the sample.
note: {bf:40.country_elec} identifies no observations in the sample.
note: {bf:44.country_elec} identifies no observations in the sample.
note: {bf:59.country_elec} identifies no observations in the sample.
note: {bf:60.country_elec} identifies no observations in the sample.
note: {bf:61.country_elec} identifies no observations in the sample.
note: {bf:65.country_elec} identifies no observations in the sample.
note: {bf:66.country_elec} identifies no observations in the sample.
note: {bf:98.country_elec} identifies no observations in the sample.
note: {bf:101.country_elec} omitted because of collinearity.
note: {bf:104.country_elec} identifies no observations in the sample.
note: {bf:114.country_elec} identifies no observations in the sample.
note: {bf:115.country_elec} identifies no observations in the sample.
note: {bf:116.country_elec} identifies no observations in the sample.
note: {bf:117.country_elec} identifies no observations in the sample.
note: {bf:120.country_elec} omitted because of collinearity.
note: {bf:149.country_elec} identifies no observations in the sample.
note: {bf:150.country_elec} identifies no observations in the sample.
note: {bf:151.country_elec} identifies no observations in the sample.
note: {bf:152.country_elec} identifies no observations in the sample.
note: {bf:164.country_elec} identifies no observations in the sample.
note: {bf:168.country_elec} omitted because of collinearity.
note: {bf:169.country_elec} omitted because of collinearity.
note: {bf:170.country_elec} identifies no observations in the sample.
note: {bf:171.country_elec} omitted because of collinearity.

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-579.44942}  
Iteration 1:{space 3}log likelihood = {res:-578.76273}  
Iteration 2:{space 3}log likelihood = {res:-578.76253}  
Iteration 3:{space 3}log likelihood = {res:-578.76253}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-591.91224}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-591.91224}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-587.24513}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-582.39226}  (not concave)
Iteration 3:{space 3}log pseudolikelihood = {res:-579.74618}  (not concave)
Iteration 4:{space 3}log pseudolikelihood = {res:-579.17532}  (not concave)
Iteration 5:{space 3}log pseudolikelihood = {res:-578.93903}  (not concave)
Iteration 6:{space 3}log pseudolikelihood = {res:-578.84315}  (not concave)
Iteration 7:{space 3}log pseudolikelihood = {res:-578.76553}  (not concave)
Iteration 8:{space 3}log pseudolikelihood = {res:-578.76359}  (not concave)
Iteration 9:{space 3}log pseudolikelihood = {res: -578.7632}  (not concave)
Iteration 10:{space 2}log pseudolikelihood = {res:-578.76312}  (not concave)
Iteration 11:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 12:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 13:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 14:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 15:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 16:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 17:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 18:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 19:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 20:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 21:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 22:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 23:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 24:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 25:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 26:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 27:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 28:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 29:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 30:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 31:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 32:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 33:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 34:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 35:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 36:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 37:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 38:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 39:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 40:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 41:{space 2}log pseudolikelihood = {res:-578.76309}  (not concave)
Iteration 42:{space 2}log pseudolikelihood = {res:-578.76309}  (backed up)
Iteration 43:{space 2}log pseudolikelihood = {res:-578.76308}  (not concave)
Iteration 44:{space 2}log pseudolikelihood = {res:-578.76308}  (backed up)
Iteration 45:{space 2}log pseudolikelihood = {res:-578.76308}  (backed up)
Iteration 46:{space 2}log pseudolikelihood = {res:-578.76307}  (backed up)
Iteration 47:{space 2}log pseudolikelihood = {res:-578.76306}  (backed up)
Iteration 48:{space 2}log pseudolikelihood = {res:-578.76302}  (not concave)
Iteration 49:{space 2}log pseudolikelihood = {res:-578.76302}  
Iteration 50:{space 2}log pseudolikelihood = {res:-578.76253}  
Iteration 51:{space 2}log pseudolikelihood = {res:-578.76253}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     1,039
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={res}{col 69}        10

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         3
{col 63}{txt}avg{col 67}={res}{col 69}     103.9
{col 63}{txt}max{col 67}={res}{col 69}       307

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}        .
{txt}Log pseudolikelihood = {res}-578.76253{col 49}{txt}Prob > chi2{col 67}={res}{col 73}     .
{txt}{ralign 91:(Std. err. adjusted for {res:10} clusters in {res:ccode})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}    c_mainstream_vs_green{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8401039{col 39}{space 2} .1408693{col 50}{space 1}   -1.04{col 59}{space 3}0.299{col 67}{space 4} .6047875{col 80}{space 3} 1.166979
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9997196{col 39}{space 2} .0062984{col 50}{space 1}   -0.04{col 59}{space 3}0.964{col 67}{space 4} .9874508{col 80}{space 3} 1.012141
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} .8717929{col 39}{space 2} .3279131{col 50}{space 1}   -0.36{col 59}{space 3}0.715{col 67}{space 4}  .417105{col 80}{space 3} 1.822138
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} .8244206{col 39}{space 2} .3515085{col 50}{space 1}   -0.45{col 59}{space 3}0.651{col 67}{space 4} .3574553{col 80}{space 3} 1.901411
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 1.202798{col 39}{space 2}  .127588{col 50}{space 1}    1.74{col 59}{space 3}0.082{col 67}{space 4} .9770136{col 80}{space 3}  1.48076
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .9192242{col 39}{space 2} .1577751{col 50}{space 1}   -0.49{col 59}{space 3}0.624{col 67}{space 4} .6566315{col 80}{space 3}  1.28683
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.034727{col 39}{space 2} .2208554{col 50}{space 1}    0.16{col 59}{space 3}0.873{col 67}{space 4} .6809931{col 80}{space 3} 1.572204
{txt}{space 25} {c |}
{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} 1.217995{col 39}{space 2} .0615828{col 50}{space 1}    3.90{col 59}{space 3}0.000{col 67}{space 4} 1.103083{col 80}{space 3} 1.344878
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .5336891{col 39}{space 2} .1377873{col 50}{space 1}   -2.43{col 59}{space 3}0.015{col 67}{space 4} .3217555{col 80}{space 3} .8852188
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .8397076{col 39}{space 2} .1649815{col 50}{space 1}   -0.89{col 59}{space 3}0.374{col 67}{space 4} .5713318{col 80}{space 3}  1.23415
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.006853{col 39}{space 2} .0225371{col 50}{space 1}    0.31{col 59}{space 3}0.760{col 67}{space 4} .9636361{col 80}{space 3} 1.052008
{txt}{space 3}lvotetotgreen_combined {c |}{col 27}{res}{space 2} .8971488{col 39}{space 2} .0324113{col 50}{space 1}   -3.00{col 59}{space 3}0.003{col 67}{space 4} .8358206{col 80}{space 3} .9629769
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.069247{col 39}{space 2} .0329011{col 50}{space 1}    2.18{col 59}{space 3}0.030{col 67}{space 4} 1.006668{col 80}{space 3} 1.135716
{txt}{space 25} {c |}
{space 13}country_elec {c |}
{space 23}7  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}23  {c |}{col 27}{res}{space 2} 1.953163{col 39}{space 2} .2163825{col 50}{space 1}    6.04{col 59}{space 3}0.000{col 67}{space 4} 1.571946{col 80}{space 3} 2.426831
{txt}{space 22}24  {c |}{col 27}{res}{space 2} .1475369{col 39}{space 2} .0364475{col 50}{space 1}   -7.75{col 59}{space 3}0.000{col 67}{space 4} .0909117{col 80}{space 3} .2394317
{txt}{space 22}25  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}26  {c |}{col 27}{res}{space 2} 1.549281{col 39}{space 2} .3960746{col 50}{space 1}    1.71{col 59}{space 3}0.087{col 67}{space 4} .9386853{col 80}{space 3} 2.557057
{txt}{space 22}36  {c |}{col 27}{res}{space 2} .4690578{col 39}{space 2}  .174109{col 50}{space 1}   -2.04{col 59}{space 3}0.041{col 67}{space 4} .2266052{col 80}{space 3} .9709187
{txt}{space 22}37  {c |}{col 27}{res}{space 2} .5789436{col 39}{space 2} .1802405{col 50}{space 1}   -1.76{col 59}{space 3}0.079{col 67}{space 4} .3145101{col 80}{space 3} 1.065707
{txt}{space 22}40  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}43  {c |}{col 27}{res}{space 2} .6698139{col 39}{space 2} .1519474{col 50}{space 1}   -1.77{col 59}{space 3}0.077{col 67}{space 4} .4293969{col 80}{space 3} 1.044839
{txt}{space 22}44  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}45  {c |}{col 27}{res}{space 2} .5800096{col 39}{space 2} .0724129{col 50}{space 1}   -4.36{col 59}{space 3}0.000{col 67}{space 4} .4541137{col 80}{space 3} .7408083
{txt}{space 22}59  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}60  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}61  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}62  {c |}{col 27}{res}{space 2} .4004424{col 39}{space 2} .0599315{col 50}{space 1}   -6.11{col 59}{space 3}0.000{col 67}{space 4} .2986391{col 80}{space 3} .5369497
{txt}{space 22}64  {c |}{col 27}{res}{space 2} .6623724{col 39}{space 2} .1628751{col 50}{space 1}   -1.68{col 59}{space 3}0.094{col 67}{space 4} .4090672{col 80}{space 3} 1.072531
{txt}{space 22}65  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}66  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 22}67  {c |}{col 27}{res}{space 2} .8987881{col 39}{space 2} .1744018{col 50}{space 1}   -0.55{col 59}{space 3}0.582{col 67}{space 4} .6144538{col 80}{space 3} 1.314696
{txt}{space 22}74  {c |}{col 27}{res}{space 2} .8680107{col 39}{space 2} .2522032{col 50}{space 1}   -0.49{col 59}{space 3}0.626{col 67}{space 4} .4911406{col 80}{space 3} 1.534067
{txt}{space 22}84  {c |}{col 27}{res}{space 2} .3488614{col 39}{space 2} .1505165{col 50}{space 1}   -2.44{col 59}{space 3}0.015{col 67}{space 4}  .149762{col 80}{space 3} .8126515
{txt}{space 22}98  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}101  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}102  {c |}{col 27}{res}{space 2} .7211517{col 39}{space 2} .2237537{col 50}{space 1}   -1.05{col 59}{space 3}0.292{col 67}{space 4} .3925744{col 80}{space 3} 1.324742
{txt}{space 21}104  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}114  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}115  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}116  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}117  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}118  {c |}{col 27}{res}{space 2} .7604967{col 39}{space 2}       .1{col 50}{space 1}   -2.08{col 59}{space 3}0.037{col 67}{space 4} .5877197{col 80}{space 3} .9840664
{txt}{space 21}119  {c |}{col 27}{res}{space 2} .5719349{col 39}{space 2} .1230579{col 50}{space 1}   -2.60{col 59}{space 3}0.009{col 67}{space 4} .3751471{col 80}{space 3} .8719501
{txt}{space 21}120  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}149  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}150  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}151  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}152  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}164  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}168  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}169  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 21}170  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (empty)
{space 21}171  {c |}{col 27}{res}{space 2}        1{col 39}{txt}  (omitted)
{space 25} {c |}
{space 20}_cons {c |}{col 27}{res}{space 2}  1.33052{col 39}{space 2} 1.019454{col 50}{space 1}    0.37{col 59}{space 3}0.709{col 67}{space 4} .2963637{col 80}{space 3} 5.973351
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}ccode                    {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} 6.02e-35{col 39}{space 2} 8.83e-35{col 67}{space 4} 3.39e-36{col 80}{space 3} 1.07e-33
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. est store M6
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}10
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea19.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") scalar(N_elections) title(Table A19. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea19.rtf"'})

{com}. 
. *************
. **Figure A4**
. *************
. 
. twoway (scatter lpss_mod3_upd lcorporatism_z_sm537) (lfit lpss_mod3_upd lcorporatism_z_sm537), xtitle("Corporatism t-1") ytitle("Party system saturation t-1") scheme(plotplain) legend(off) name(a, replace)
{res}{txt}
{com}. twoway (scatter lpss_mod3_upd n_sharedrule) (lfit lpss_mod3_upd n_sharedrule), xtitle("Federalism (shared rule) t-1") ytitle("Party system saturation t-1") scheme(plotplain) legend(off) name(b, replace)
{res}{txt}
{com}. twoway (scatter lpss_mod3_upd n_RAI) (lfit lpss_mod3_upd n_RAI), xtitle("Federalism (RAI) t-1") ytitle("Party system saturation t-1") scheme(plotplain) legend(off) name(c, replace)
{res}{txt}
{com}. 
. graph combine a b c, scheme(plotplain)
{res}{txt}
{com}. graph export "figurea4.tif", replace
{txt}{p 0 4 2}
file {bf}
figurea4.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. *************
. **Table A20**
. *************
. 
. /*Corporatism as instrument*/
. 
. ivprobit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined (lpss_mod3_upd=lcorporatism_z_sm537) if p_niche==0, first vce(cluster country_elec) 
{res}
{txt}Fitting exogenous probit model

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-7186.8104}  
Iteration 1:{space 3}log likelihood = {res:-6688.8622}  
Iteration 2:{space 3}log likelihood = {res:-6675.8358}  
Iteration 3:{space 3}log likelihood = {res:-6675.8016}  
Iteration 4:{space 3}log likelihood = {res:-6675.8016}  
{res}
{txt}Fitting full model

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-34301.221}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34301.219}  

{col 1}Probit model with endogenous regressors{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:25,165}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:494.49}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-34301.219}{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}

{txt}{ralign 97:(Std. err. adjusted for {res:38} clusters in {res:country_elec})}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 33}{c |} Coefficient{col 45}  std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}c_niche                         {txt}{c |}
{space 18}lpss_mod3_upd {c |}{col 33}{res}{space 2} .1562679{col 45}{space 2} .0925455{col 56}{space 1}    1.69{col 65}{space 3}0.091{col 73}{space 4} -.025118{col 86}{space 3} .3376538
{txt}{space 27}male {c |}{col 33}{res}{space 2} .0364281{col 45}{space 2} .0315504{col 56}{space 1}    1.15{col 65}{space 3}0.248{col 73}{space 4}-.0254095{col 86}{space 3} .0982657
{txt}{space 28}age {c |}{col 33}{res}{space 2}-.0051879{col 45}{space 2} .0014328{col 56}{space 1}   -3.62{col 65}{space 3}0.000{col 73}{space 4}-.0079961{col 86}{space 3}-.0023797
{txt}{space 31} {c |}
{space 24}highedu {c |}
{space 11}Secondary education  {c |}{col 33}{res}{space 2} .3293156{col 45}{space 2} .0667418{col 56}{space 1}    4.93{col 65}{space 3}0.000{col 73}{space 4} .1985041{col 86}{space 3}  .460127
{txt}{space 6}Post-secondary education  {c |}{col 33}{res}{space 2} .4482055{col 45}{space 2} .0857807{col 56}{space 1}    5.23{col 65}{space 3}0.000{col 73}{space 4} .2800785{col 86}{space 3} .6163326
{txt}{space 31} {c |}
{space 20}income_3cat {c |}
{space 17}Medium income  {c |}{col 33}{res}{space 2}-.1098367{col 45}{space 2} .0324208{col 56}{space 1}   -3.39{col 65}{space 3}0.001{col 73}{space 4}-.1733803{col 86}{space 3}-.0462931
{txt}{space 19}High income  {c |}{col 33}{res}{space 2}-.2775437{col 45}{space 2} .0486816{col 56}{space 1}   -5.70{col 65}{space 3}0.000{col 73}{space 4}-.3729578{col 86}{space 3}-.1821296
{txt}{space 31} {c |}
{space 19}dissatisfied {c |}{col 33}{res}{space 2} .3619477{col 45}{space 2}  .053909{col 56}{space 1}    6.71{col 65}{space 3}0.000{col 73}{space 4} .2562879{col 86}{space 3} .4676075
{txt}{space 11}distpreviouspartycmp {c |}{col 33}{res}{space 2}-.0149838{col 45}{space 2} .0165669{col 56}{space 1}   -0.90{col 65}{space 3}0.366{col 73}{space 4}-.0474542{col 86}{space 3} .0174867
{txt}{space 21}closeparty {c |}{col 33}{res}{space 2}-.4621902{col 45}{space 2} .0543168{col 56}{space 1}   -8.51{col 65}{space 3}0.000{col 73}{space 4}-.5686491{col 86}{space 3}-.3557313
{txt}{space 19}p_government {c |}{col 33}{res}{space 2} .1306395{col 45}{space 2} .0973442{col 56}{space 1}    1.34{col 65}{space 3}0.180{col 73}{space 4}-.0601516{col 86}{space 3} .3214306
{txt}{space 24}sd_rile {c |}{col 33}{res}{space 2} .0071069{col 45}{space 2} .0061624{col 56}{space 1}    1.15{col 65}{space 3}0.249{col 73}{space 4}-.0049712{col 86}{space 3}  .019185
{txt}{space 9}lvotetotniche_combined {c |}{col 33}{res}{space 2} .0062208{col 45}{space 2} .0037161{col 56}{space 1}    1.67{col 65}{space 3}0.094{col 73}{space 4}-.0010626{col 86}{space 3} .0135042
{txt}{space 26}_cons {c |}{col 33}{res}{space 2}-1.525441{col 45}{space 2} .1662743{col 56}{space 1}   -9.17{col 65}{space 3}0.000{col 73}{space 4}-1.851333{col 86}{space 3} -1.19955
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lpss_mod3_upd                   {txt}{c |}
{space 27}male {c |}{col 33}{res}{space 2} .0066505{col 45}{space 2} .0102958{col 56}{space 1}    0.65{col 65}{space 3}0.518{col 73}{space 4}-.0135288{col 86}{space 3} .0268298
{txt}{space 28}age {c |}{col 33}{res}{space 2}-.0025245{col 45}{space 2} .0019802{col 56}{space 1}   -1.27{col 65}{space 3}0.202{col 73}{space 4}-.0064057{col 86}{space 3} .0013567
{txt}{space 31} {c |}
{space 24}highedu {c |}
{space 11}Secondary education  {c |}{col 33}{res}{space 2}-.1292993{col 45}{space 2} .1243348{col 56}{space 1}   -1.04{col 65}{space 3}0.298{col 73}{space 4} -.372991{col 86}{space 3} .1143924
{txt}{space 6}Post-secondary education  {c |}{col 33}{res}{space 2}-.2848403{col 45}{space 2} .1754565{col 56}{space 1}   -1.62{col 65}{space 3}0.104{col 73}{space 4}-.6287288{col 86}{space 3} .0590482
{txt}{space 31} {c |}
{space 20}income_3cat {c |}
{space 17}Medium income  {c |}{col 33}{res}{space 2} .0132999{col 45}{space 2} .0260178{col 56}{space 1}    0.51{col 65}{space 3}0.609{col 73}{space 4}-.0376941{col 86}{space 3} .0642939
{txt}{space 19}High income  {c |}{col 33}{res}{space 2} .1092173{col 45}{space 2} .0610371{col 56}{space 1}    1.79{col 65}{space 3}0.074{col 73}{space 4}-.0104132{col 86}{space 3} .2288479
{txt}{space 31} {c |}
{space 19}dissatisfied {c |}{col 33}{res}{space 2}-.0549209{col 45}{space 2} .0642192{col 56}{space 1}   -0.86{col 65}{space 3}0.392{col 73}{space 4}-.1807883{col 86}{space 3} .0709464
{txt}{space 11}distpreviouspartycmp {c |}{col 33}{res}{space 2}-.0123401{col 45}{space 2} .0308015{col 56}{space 1}   -0.40{col 65}{space 3}0.689{col 73}{space 4}-.0727099{col 86}{space 3} .0480297
{txt}{space 21}closeparty {c |}{col 33}{res}{space 2}-.0262761{col 45}{space 2} .0556191{col 56}{space 1}   -0.47{col 65}{space 3}0.637{col 73}{space 4}-.1352876{col 86}{space 3} .0827353
{txt}{space 19}p_government {c |}{col 33}{res}{space 2}-.0442821{col 45}{space 2} .0714339{col 56}{space 1}   -0.62{col 65}{space 3}0.535{col 73}{space 4}-.1842899{col 86}{space 3} .0957257
{txt}{space 24}sd_rile {c |}{col 33}{res}{space 2}-.0057025{col 45}{space 2} .0175323{col 56}{space 1}   -0.33{col 65}{space 3}0.745{col 73}{space 4}-.0400652{col 86}{space 3} .0286601
{txt}{space 9}lvotetotniche_combined {c |}{col 33}{res}{space 2} .0126206{col 45}{space 2}  .007597{col 56}{space 1}    1.66{col 65}{space 3}0.097{col 73}{space 4}-.0022692{col 86}{space 3} .0275103
{txt}{space 11}lcorporatism_z_sm537 {c |}{col 33}{res}{space 2} 1.678725{col 45}{space 2} .5218122{col 56}{space 1}    3.22{col 65}{space 3}0.001{col 73}{space 4} .6559915{col 86}{space 3} 2.701458
{txt}{space 26}_cons {c |}{col 33}{res}{space 2}-.4979213{col 45}{space 2} .3926581{col 56}{space 1}   -1.27{col 65}{space 3}0.205{col 73}{space 4}-1.267517{col 86}{space 3} .2716744
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/athrho2_1 {c |}{col 33}{res}{space 2}-.1338415{col 45}{space 2} .0997481{col 56}{space 1}   -1.34{col 65}{space 3}0.180{col 73}{space 4}-.3293443{col 86}{space 3} .0616612
{txt}{space 22}/lnsigma2 {c |}{col 33}{res}{space 2}-.3211672{col 45}{space 2} .1242666{col 56}{space 1}   -2.58{col 65}{space 3}0.010{col 73}{space 4}-.5647254{col 86}{space 3}-.0776091
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
 corr(e.lpss_mod3_upd,e.c_niche){c |}{col 33}{res}{space 2} -.133048{col 45}{space 2} .0979824{col 73}{space 4}-.3179315{col 86}{space 3} .0615832
{txt}             sd(e.lpss_mod3_upd){c |}{col 33}{res}{space 2}  .725302{col 45}{space 2} .0901308{col 73}{space 4} .5685163{col 86}{space 3} .9253261
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of exogeneity (corr = 0): chi2({res}1{txt}) = {res}1.80{txt}{col 59}Prob > chi2 = {res}0.1797
{txt}{p 0 14 86}Instrumented: {res:lpss_mod3_upd}{p_end}
{p 0 14 86}{space 1}Instruments: {res:male age 2.highedu 3.highedu 2.income_3cat 3.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lcorporatism_z_sm537}{p_end}

{com}. //t=3.22
. display 3.22^2 
{res}10.3684
{txt}
{com}. est store M1
{txt}
{com}. ivprobit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined (lpss_mod3_upd=lcorporatism_z_sm537) if p_niche==1, first vce(cluster country_elec)
{res}
{txt}Fitting exogenous probit model

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2344.6009}  
Iteration 1:{space 3}log likelihood = {res: -2226.192}  
Iteration 2:{space 3}log likelihood = {res:-2225.4264}  
Iteration 3:{space 3}log likelihood = {res:-2225.4263}  
{res}
{txt}Fitting full model

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-7200.9204}  
Iteration 1:{space 3}log pseudolikelihood = {res:-7200.9094}  
Iteration 2:{space 3}log pseudolikelihood = {res:-7200.9094}  

{col 1}Probit model with endogenous regressors{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:4,470}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:233.22}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-7200.9094}{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}

{txt}{ralign 102:(Std. err. adjusted for {res:31} clusters in {res:country_elec})}
{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 38}{c |} Coefficient{col 50}  std. err.{col 62}      z{col 70}   P>|z|{col 78}     [95% con{col 91}f. interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}c_mainstream                         {txt}{c |}
{space 23}lpss_mod3_upd {c |}{col 38}{res}{space 2}-.1345991{col 50}{space 2} .0861118{col 61}{space 1}   -1.56{col 70}{space 3}0.118{col 78}{space 4} -.303375{col 91}{space 3} .0341769
{txt}{space 32}male {c |}{col 38}{res}{space 2}-.0969462{col 50}{space 2} .0627723{col 61}{space 1}   -1.54{col 70}{space 3}0.122{col 78}{space 4}-.2199776{col 91}{space 3} .0260852
{txt}{space 33}age {c |}{col 38}{res}{space 2}-.0037644{col 50}{space 2} .0016732{col 61}{space 1}   -2.25{col 70}{space 3}0.024{col 78}{space 4}-.0070439{col 91}{space 3} -.000485
{txt}{space 36} {c |}
{space 29}highedu {c |}
{space 16}Secondary education  {c |}{col 38}{res}{space 2} .1614791{col 50}{space 2} .0641495{col 61}{space 1}    2.52{col 70}{space 3}0.012{col 78}{space 4} .0357484{col 91}{space 3} .2872097
{txt}{space 11}Post-secondary education  {c |}{col 38}{res}{space 2}-.0142164{col 50}{space 2} .0972121{col 61}{space 1}   -0.15{col 70}{space 3}0.884{col 78}{space 4}-.2047486{col 91}{space 3} .1763157
{txt}{space 36} {c |}
{space 25}income_3cat {c |}
{space 22}Medium income  {c |}{col 38}{res}{space 2} .0651275{col 50}{space 2} .0590887{col 61}{space 1}    1.10{col 70}{space 3}0.270{col 78}{space 4}-.0506842{col 91}{space 3} .1809392
{txt}{space 24}High income  {c |}{col 38}{res}{space 2} .2801463{col 50}{space 2} .0724187{col 61}{space 1}    3.87{col 70}{space 3}0.000{col 78}{space 4} .1382082{col 91}{space 3} .4220844
{txt}{space 36} {c |}
{space 24}dissatisfied {c |}{col 38}{res}{space 2}-.1832608{col 50}{space 2} .0454351{col 61}{space 1}   -4.03{col 70}{space 3}0.000{col 78}{space 4}-.2723121{col 91}{space 3}-.0942096
{txt}{space 16}distpreviouspartycmp {c |}{col 38}{res}{space 2} .0728611{col 50}{space 2} .0230174{col 61}{space 1}    3.17{col 70}{space 3}0.002{col 78}{space 4} .0277479{col 91}{space 3} .1179743
{txt}{space 26}closeparty {c |}{col 38}{res}{space 2} -.476193{col 50}{space 2} .0560728{col 61}{space 1}   -8.49{col 70}{space 3}0.000{col 78}{space 4}-.5860938{col 91}{space 3}-.3662923
{txt}{space 24}p_government {c |}{col 38}{res}{space 2} -.180189{col 50}{space 2} .0769666{col 61}{space 1}   -2.34{col 70}{space 3}0.019{col 78}{space 4}-.3310408{col 91}{space 3}-.0293373
{txt}{space 29}sd_rile {c |}{col 38}{res}{space 2}-.0064632{col 50}{space 2} .0067471{col 61}{space 1}   -0.96{col 70}{space 3}0.338{col 78}{space 4}-.0196873{col 91}{space 3} .0067608
{txt}{space 14}lvotetotniche_combined {c |}{col 38}{res}{space 2} .0020198{col 50}{space 2} .0042244{col 61}{space 1}    0.48{col 70}{space 3}0.633{col 78}{space 4}-.0062598{col 91}{space 3} .0102994
{txt}{space 31}_cons {c |}{col 38}{res}{space 2}-.4152312{col 50}{space 2} .1614348{col 61}{space 1}   -2.57{col 70}{space 3}0.010{col 78}{space 4}-.7316376{col 91}{space 3}-.0988249
{txt}{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lpss_mod3_upd                        {txt}{c |}
{space 32}male {c |}{col 38}{res}{space 2} .0687698{col 50}{space 2}  .025471{col 61}{space 1}    2.70{col 70}{space 3}0.007{col 78}{space 4} .0188476{col 91}{space 3} .1186921
{txt}{space 33}age {c |}{col 38}{res}{space 2} .0012755{col 50}{space 2} .0032145{col 61}{space 1}    0.40{col 70}{space 3}0.692{col 78}{space 4}-.0050248{col 91}{space 3} .0075757
{txt}{space 36} {c |}
{space 29}highedu {c |}
{space 16}Secondary education  {c |}{col 38}{res}{space 2} .0669986{col 50}{space 2} .1893452{col 61}{space 1}    0.35{col 70}{space 3}0.723{col 78}{space 4}-.3041113{col 91}{space 3} .4381084
{txt}{space 11}Post-secondary education  {c |}{col 38}{res}{space 2}-.1997852{col 50}{space 2} .2053227{col 61}{space 1}   -0.97{col 70}{space 3}0.331{col 78}{space 4}-.6022102{col 91}{space 3} .2026398
{txt}{space 36} {c |}
{space 25}income_3cat {c |}
{space 22}Medium income  {c |}{col 38}{res}{space 2} .0161848{col 50}{space 2} .0564279{col 61}{space 1}    0.29{col 70}{space 3}0.774{col 78}{space 4}-.0944118{col 91}{space 3} .1267815
{txt}{space 24}High income  {c |}{col 38}{res}{space 2} .1273798{col 50}{space 2} .0843718{col 61}{space 1}    1.51{col 70}{space 3}0.131{col 78}{space 4}-.0379858{col 91}{space 3} .2927455
{txt}{space 36} {c |}
{space 24}dissatisfied {c |}{col 38}{res}{space 2} .0843948{col 50}{space 2} .1077257{col 61}{space 1}    0.78{col 70}{space 3}0.433{col 78}{space 4}-.1267438{col 91}{space 3} .2955333
{txt}{space 16}distpreviouspartycmp {c |}{col 38}{res}{space 2}-.0094879{col 50}{space 2} .0238353{col 61}{space 1}   -0.40{col 70}{space 3}0.691{col 78}{space 4}-.0562042{col 91}{space 3} .0372284
{txt}{space 26}closeparty {c |}{col 38}{res}{space 2} .0923004{col 50}{space 2} .0653695{col 61}{space 1}    1.41{col 70}{space 3}0.158{col 78}{space 4}-.0358215{col 91}{space 3} .2204223
{txt}{space 24}p_government {c |}{col 38}{res}{space 2}-.0182228{col 50}{space 2} .2467473{col 61}{space 1}   -0.07{col 70}{space 3}0.941{col 78}{space 4}-.5018386{col 91}{space 3}  .465393
{txt}{space 29}sd_rile {c |}{col 38}{res}{space 2} .0094578{col 50}{space 2} .0204824{col 61}{space 1}    0.46{col 70}{space 3}0.644{col 78}{space 4}-.0306871{col 91}{space 3} .0496026
{txt}{space 14}lvotetotniche_combined {c |}{col 38}{res}{space 2} .0024903{col 50}{space 2} .0112139{col 61}{space 1}    0.22{col 70}{space 3}0.824{col 78}{space 4}-.0194885{col 91}{space 3} .0244691
{txt}{space 16}lcorporatism_z_sm537 {c |}{col 38}{res}{space 2} 2.089883{col 50}{space 2} .5376708{col 61}{space 1}    3.89{col 70}{space 3}0.000{col 78}{space 4} 1.036068{col 91}{space 3} 3.143699
{txt}{space 31}_cons {c |}{col 38}{res}{space 2}-1.018058{col 50}{space 2} .5043518{col 61}{space 1}   -2.02{col 70}{space 3}0.044{col 78}{space 4}-2.006569{col 91}{space 3}-.0295467
{txt}{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}/athrho2_1 {c |}{col 38}{res}{space 2} .1015056{col 50}{space 2}  .087536{col 61}{space 1}    1.16{col 70}{space 3}0.246{col 78}{space 4}-.0700619{col 91}{space 3} .2730731
{txt}{space 27}/lnsigma2 {c |}{col 38}{res}{space 2}-.3058551{col 50}{space 2} .1497964{col 61}{space 1}   -2.04{col 70}{space 3}0.041{col 78}{space 4}-.5994506{col 91}{space 3}-.0122595
{txt}{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
 corr(e.lpss_mod3_upd,e.c_mainstream){c |}{col 38}{res}{space 2} .1011584{col 50}{space 2} .0866403{col 78}{space 4}-.0699475{col 91}{space 3}  .266482
{txt}                  sd(e.lpss_mod3_upd){c |}{col 38}{res}{space 2} .7364934{col 50}{space 2} .1103241{col 78}{space 4} .5491132{col 91}{space 3} .9878153
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of exogeneity (corr = 0): chi2({res}1{txt}) = {res}1.34{txt}{col 59}Prob > chi2 = {res}0.2462
{txt}{p 0 14 81}Instrumented: {res:lpss_mod3_upd}{p_end}
{p 0 14 81}{space 1}Instruments: {res:male age 2.highedu 3.highedu 2.income_3cat 3.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lcorporatism_z_sm537}{p_end}

{com}. //t=3.89
. display 3.89^2
{res}15.1321
{txt}
{com}. est store M2
{txt}
{com}. ivprobit c_radicalrl_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined (lpss_mod3_upd=lcorporatism_z_sm537) if p_radicalrl_vs_mainstream==0, first vce(cluster country_elec)
{res}
{txt}Fitting exogenous probit model

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-5019.7513}  
Iteration 1:{space 3}log likelihood = {res:-4557.9281}  
Iteration 2:{space 3}log likelihood = {res:-4528.4886}  
Iteration 3:{space 3}log likelihood = {res: -4528.357}  
Iteration 4:{space 3}log likelihood = {res: -4528.357}  
{res}
{txt}Fitting full model

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-30069.165}  
Iteration 1:{space 3}log pseudolikelihood = {res:-30069.163}  

{col 1}Probit model with endogenous regressors{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:24,362}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:442.93}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-30069.163}{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}

{txt}{ralign 115:(Std. err. adjusted for {res:38} clusters in {res:country_elec})}
{hline 50}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 51}{c |}{col 63}    Robust
{col 51}{c |} Coefficient{col 63}  std. err.{col 75}      z{col 83}   P>|z|{col 91}     [95% con{col 104}f. interval]
{hline 50}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}c_radicalrl_vs_mainstream                         {txt}{c |}
{space 36}lpss_mod3_upd {c |}{col 51}{res}{space 2} .3782989{col 63}{space 2}  .161785{col 74}{space 1}    2.34{col 83}{space 3}0.019{col 91}{space 4} .0612062{col 104}{space 3} .6953916
{txt}{space 45}male {c |}{col 51}{res}{space 2} .1218931{col 63}{space 2} .0310498{col 74}{space 1}    3.93{col 83}{space 3}0.000{col 91}{space 4} .0610367{col 104}{space 3} .1827495
{txt}{space 46}age {c |}{col 51}{res}{space 2}-.0030082{col 63}{space 2} .0019083{col 74}{space 1}   -1.58{col 83}{space 3}0.115{col 91}{space 4}-.0067485{col 104}{space 3} .0007321
{txt}{space 49} {c |}
{space 42}highedu {c |}
{space 29}Secondary education  {c |}{col 51}{res}{space 2} .4295292{col 63}{space 2} .0978144{col 74}{space 1}    4.39{col 83}{space 3}0.000{col 91}{space 4} .2378165{col 104}{space 3}  .621242
{txt}{space 24}Post-secondary education  {c |}{col 51}{res}{space 2} .4960258{col 63}{space 2} .1249488{col 74}{space 1}    3.97{col 83}{space 3}0.000{col 91}{space 4} .2511307{col 104}{space 3}  .740921
{txt}{space 49} {c |}
{space 37}dissatisfied {c |}{col 51}{res}{space 2} .4403443{col 63}{space 2} .0617466{col 74}{space 1}    7.13{col 83}{space 3}0.000{col 91}{space 4} .3193231{col 104}{space 3} .5613654
{txt}{space 49} {c |}
{space 38}income_3cat {c |}
{space 35}Medium income  {c |}{col 51}{res}{space 2}-.1635267{col 63}{space 2} .0368048{col 74}{space 1}   -4.44{col 83}{space 3}0.000{col 91}{space 4}-.2356627{col 104}{space 3}-.0913907
{txt}{space 37}High income  {c |}{col 51}{res}{space 2} -.428121{col 63}{space 2} .0590897{col 74}{space 1}   -7.25{col 83}{space 3}0.000{col 91}{space 4}-.5439347{col 104}{space 3}-.3123074
{txt}{space 49} {c |}
{space 29}distpreviouspartycmp {c |}{col 51}{res}{space 2}-.0002889{col 63}{space 2} .0186803{col 74}{space 1}   -0.02{col 83}{space 3}0.988{col 91}{space 4}-.0369016{col 104}{space 3} .0363238
{txt}{space 39}closeparty {c |}{col 51}{res}{space 2}-.4397099{col 63}{space 2} .0735938{col 74}{space 1}   -5.97{col 83}{space 3}0.000{col 91}{space 4}-.5839511{col 104}{space 3}-.2954688
{txt}{space 37}p_government {c |}{col 51}{res}{space 2} .0660129{col 63}{space 2} .1091601{col 74}{space 1}    0.60{col 83}{space 3}0.545{col 91}{space 4}-.1479371{col 104}{space 3} .2799628
{txt}{space 42}sd_rile {c |}{col 51}{res}{space 2} .0019043{col 63}{space 2} .0095304{col 74}{space 1}    0.20{col 83}{space 3}0.842{col 91}{space 4}-.0167751{col 104}{space 3} .0205836
{txt}{space 23}lvotetotradicallr_combined {c |}{col 51}{res}{space 2}  .005359{col 63}{space 2} .0085366{col 74}{space 1}    0.63{col 83}{space 3}0.530{col 91}{space 4}-.0113724{col 104}{space 3} .0220903
{txt}{space 44}_cons {c |}{col 51}{res}{space 2}-1.783929{col 63}{space 2} .2810221{col 74}{space 1}   -6.35{col 83}{space 3}0.000{col 91}{space 4}-2.334722{col 104}{space 3}-1.233136
{txt}{hline 50}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lpss_mod3_upd                                     {txt}{c |}
{space 45}male {c |}{col 51}{res}{space 2}-.0051621{col 63}{space 2} .0104416{col 74}{space 1}   -0.49{col 83}{space 3}0.621{col 91}{space 4}-.0256274{col 104}{space 3} .0153031
{txt}{space 46}age {c |}{col 51}{res}{space 2}-.0025634{col 63}{space 2} .0020006{col 74}{space 1}   -1.28{col 83}{space 3}0.200{col 91}{space 4}-.0064846{col 104}{space 3} .0013578
{txt}{space 49} {c |}
{space 42}highedu {c |}
{space 29}Secondary education  {c |}{col 51}{res}{space 2}-.1184106{col 63}{space 2} .1246143{col 74}{space 1}   -0.95{col 83}{space 3}0.342{col 91}{space 4}-.3626502{col 104}{space 3}  .125829
{txt}{space 24}Post-secondary education  {c |}{col 51}{res}{space 2}-.3308292{col 63}{space 2} .1661197{col 74}{space 1}   -1.99{col 83}{space 3}0.046{col 91}{space 4}-.6564179{col 104}{space 3}-.0052406
{txt}{space 49} {c |}
{space 37}dissatisfied {c |}{col 51}{res}{space 2}-.0097793{col 63}{space 2} .0554794{col 74}{space 1}   -0.18{col 83}{space 3}0.860{col 91}{space 4}-.1185168{col 104}{space 3} .0989583
{txt}{space 49} {c |}
{space 38}income_3cat {c |}
{space 35}Medium income  {c |}{col 51}{res}{space 2} .0377597{col 63}{space 2} .0223582{col 74}{space 1}    1.69{col 83}{space 3}0.091{col 91}{space 4}-.0060615{col 104}{space 3} .0815809
{txt}{space 37}High income  {c |}{col 51}{res}{space 2} .1536653{col 63}{space 2} .0597025{col 74}{space 1}    2.57{col 83}{space 3}0.010{col 91}{space 4} .0366506{col 104}{space 3} .2706801
{txt}{space 49} {c |}
{space 29}distpreviouspartycmp {c |}{col 51}{res}{space 2}-.0007271{col 63}{space 2}  .028047{col 74}{space 1}   -0.03{col 83}{space 3}0.979{col 91}{space 4}-.0556982{col 104}{space 3}  .054244
{txt}{space 39}closeparty {c |}{col 51}{res}{space 2}-.0518879{col 63}{space 2}   .05929{col 74}{space 1}   -0.88{col 83}{space 3}0.381{col 91}{space 4}-.1680942{col 104}{space 3} .0643184
{txt}{space 37}p_government {c |}{col 51}{res}{space 2}-.0698638{col 63}{space 2} .0667857{col 74}{space 1}   -1.05{col 83}{space 3}0.296{col 91}{space 4}-.2007614{col 104}{space 3} .0610337
{txt}{space 42}sd_rile {c |}{col 51}{res}{space 2}-.0108218{col 63}{space 2} .0166077{col 74}{space 1}   -0.65{col 83}{space 3}0.515{col 91}{space 4}-.0433723{col 104}{space 3} .0217286
{txt}{space 23}lvotetotradicallr_combined {c |}{col 51}{res}{space 2} .0311036{col 63}{space 2} .0098309{col 74}{space 1}    3.16{col 83}{space 3}0.002{col 91}{space 4} .0118354{col 104}{space 3} .0503719
{txt}{space 29}lcorporatism_z_sm537 {c |}{col 51}{res}{space 2} 1.583289{col 63}{space 2} .4639064{col 74}{space 1}    3.41{col 83}{space 3}0.001{col 91}{space 4} .6740495{col 104}{space 3} 2.492529
{txt}{space 44}_cons {c |}{col 51}{res}{space 2}-.4734472{col 63}{space 2} .3800931{col 74}{space 1}   -1.25{col 83}{space 3}0.213{col 91}{space 4}-1.218416{col 104}{space 3} .2715217
{txt}{hline 50}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 39}/athrho2_1 {c |}{col 51}{res}{space 2}-.2490508{col 63}{space 2} .1637313{col 74}{space 1}   -1.52{col 83}{space 3}0.128{col 91}{space 4}-.5699583{col 104}{space 3} .0718566
{txt}{space 40}/lnsigma2 {c |}{col 51}{res}{space 2}-.3705515{col 63}{space 2} .1237041{col 74}{space 1}   -3.00{col 83}{space 3}0.003{col 91}{space 4} -.613007{col 104}{space 3}-.1280961
{txt}{hline 50}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
 corr(e.lpss_mod3_upd,e.c_radicalrl_vs_mainstream){c |}{col 51}{res}{space 2}-.2440262{col 63}{space 2} .1539813{col 91}{space 4}-.5153287{col 104}{space 3} .0717332
{txt}                               sd(e.lpss_mod3_upd){c |}{col 51}{res}{space 2} .6903535{col 63}{space 2} .0853995{col 91}{space 4} .5417194{col 104}{space 3} .8797689
{txt}{hline 50}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of exogeneity (corr = 0): chi2({res}1{txt}) = {res}2.31{txt}{col 59}Prob > chi2 = {res}0.1282
{txt}{p 0 14 68}Instrumented: {res:lpss_mod3_upd}{p_end}
{p 0 14 68}{space 1}Instruments: {res:male age 2.highedu 3.highedu dissatisfied 2.income_3cat 3.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lcorporatism_z_sm537}{p_end}

{com}. //t=3.41
. display 3.41^2
{res}11.6281
{txt}
{com}. est store M3
{txt}
{com}. ivprobit c_mainstream_vs_radicalrl male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined (lpss_mod3_upd=lcorporatism_z_sm537) if p_radicalrl_vs_mainstream==1, first vce(cluster country_elec)
{res}
{txt}Fitting exogenous probit model

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1401.3996}  
Iteration 1:{space 3}log likelihood = {res:-1310.4003}  
Iteration 2:{space 3}log likelihood = {res:-1309.5344}  
Iteration 3:{space 3}log likelihood = {res:-1309.5344}  
{res}
{txt}Fitting full model

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-4084.1088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-4084.0906}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4084.0906}  

{col 1}Probit model with endogenous regressors{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:2,697}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:522.38}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-4084.0906}{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}

{txt}{ralign 115:(Std. err. adjusted for {res:26} clusters in {res:country_elec})}
{hline 50}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 51}{c |}{col 63}    Robust
{col 51}{c |} Coefficient{col 63}  std. err.{col 75}      z{col 83}   P>|z|{col 91}     [95% con{col 104}f. interval]
{hline 50}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}c_mainstream_vs_radicalrl                         {txt}{c |}
{space 36}lpss_mod3_upd {c |}{col 51}{res}{space 2} .0455844{col 63}{space 2} .1815814{col 74}{space 1}    0.25{col 83}{space 3}0.802{col 91}{space 4}-.3103086{col 104}{space 3} .4014774
{txt}{space 45}male {c |}{col 51}{res}{space 2}-.0863411{col 63}{space 2} .0796736{col 74}{space 1}   -1.08{col 83}{space 3}0.279{col 91}{space 4}-.2424984{col 104}{space 3} .0698162
{txt}{space 46}age {c |}{col 51}{res}{space 2}-.0048077{col 63}{space 2}  .002592{col 74}{space 1}   -1.85{col 83}{space 3}0.064{col 91}{space 4}-.0098879{col 104}{space 3} .0002724
{txt}{space 49} {c |}
{space 42}highedu {c |}
{space 29}Secondary education  {c |}{col 51}{res}{space 2} .0836447{col 63}{space 2} .1030329{col 74}{space 1}    0.81{col 83}{space 3}0.417{col 91}{space 4} -.118296{col 104}{space 3} .2855855
{txt}{space 24}Post-secondary education  {c |}{col 51}{res}{space 2}-.0998782{col 63}{space 2} .1291721{col 74}{space 1}   -0.77{col 83}{space 3}0.439{col 91}{space 4}-.3530508{col 104}{space 3} .1532945
{txt}{space 49} {c |}
{space 37}dissatisfied {c |}{col 51}{res}{space 2} -.358867{col 63}{space 2} .0515968{col 74}{space 1}   -6.96{col 83}{space 3}0.000{col 91}{space 4}-.4599949{col 104}{space 3} -.257739
{txt}{space 49} {c |}
{space 38}income_3cat {c |}
{space 35}Medium income  {c |}{col 51}{res}{space 2} .0652662{col 63}{space 2} .0860537{col 74}{space 1}    0.76{col 83}{space 3}0.448{col 91}{space 4} -.103396{col 104}{space 3} .2339283
{txt}{space 37}High income  {c |}{col 51}{res}{space 2} .2999643{col 63}{space 2} .0951669{col 74}{space 1}    3.15{col 83}{space 3}0.002{col 91}{space 4} .1134406{col 104}{space 3} .4864881
{txt}{space 49} {c |}
{space 29}distpreviouspartycmp {c |}{col 51}{res}{space 2} .0573555{col 63}{space 2} .0299308{col 74}{space 1}    1.92{col 83}{space 3}0.055{col 91}{space 4}-.0013077{col 104}{space 3} .1160187
{txt}{space 39}closeparty {c |}{col 51}{res}{space 2}-.5318415{col 63}{space 2} .0650163{col 74}{space 1}   -8.18{col 83}{space 3}0.000{col 91}{space 4}-.6592712{col 104}{space 3}-.4044118
{txt}{space 37}p_government {c |}{col 51}{res}{space 2} .0426934{col 63}{space 2} .2318889{col 74}{space 1}    0.18{col 83}{space 3}0.854{col 91}{space 4}-.4118006{col 104}{space 3} .4971873
{txt}{space 42}sd_rile {c |}{col 51}{res}{space 2}-.0103644{col 63}{space 2} .0093971{col 74}{space 1}   -1.10{col 83}{space 3}0.270{col 91}{space 4}-.0287824{col 104}{space 3} .0080536
{txt}{space 23}lvotetotradicallr_combined {c |}{col 51}{res}{space 2} -.002606{col 63}{space 2} .0097325{col 74}{space 1}   -0.27{col 83}{space 3}0.789{col 91}{space 4}-.0216815{col 104}{space 3} .0164694
{txt}{space 44}_cons {c |}{col 51}{res}{space 2}-.1328038{col 63}{space 2} .2617402{col 74}{space 1}   -0.51{col 83}{space 3}0.612{col 91}{space 4}-.6458052{col 104}{space 3} .3801976
{txt}{hline 50}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lpss_mod3_upd                                     {txt}{c |}
{space 45}male {c |}{col 51}{res}{space 2}  .019467{col 63}{space 2} .0310938{col 74}{space 1}    0.63{col 83}{space 3}0.531{col 91}{space 4}-.0414757{col 104}{space 3} .0804097
{txt}{space 46}age {c |}{col 51}{res}{space 2} .0009367{col 63}{space 2}  .003781{col 74}{space 1}    0.25{col 83}{space 3}0.804{col 91}{space 4}-.0064739{col 104}{space 3} .0083474
{txt}{space 49} {c |}
{space 42}highedu {c |}
{space 29}Secondary education  {c |}{col 51}{res}{space 2} .1510845{col 63}{space 2}  .131558{col 74}{space 1}    1.15{col 83}{space 3}0.251{col 91}{space 4}-.1067645{col 104}{space 3} .4089335
{txt}{space 24}Post-secondary education  {c |}{col 51}{res}{space 2}-.0830928{col 63}{space 2} .1322443{col 74}{space 1}   -0.63{col 83}{space 3}0.530{col 91}{space 4}-.3422869{col 104}{space 3} .1761013
{txt}{space 49} {c |}
{space 37}dissatisfied {c |}{col 51}{res}{space 2} .1512616{col 63}{space 2} .1099949{col 74}{space 1}    1.38{col 83}{space 3}0.169{col 91}{space 4}-.0643244{col 104}{space 3} .3668477
{txt}{space 49} {c |}
{space 38}income_3cat {c |}
{space 35}Medium income  {c |}{col 51}{res}{space 2} .0169537{col 63}{space 2} .0499634{col 74}{space 1}    0.34{col 83}{space 3}0.734{col 91}{space 4}-.0809728{col 104}{space 3} .1148801
{txt}{space 37}High income  {c |}{col 51}{res}{space 2} .1002079{col 63}{space 2}  .066312{col 74}{space 1}    1.51{col 83}{space 3}0.131{col 91}{space 4}-.0297611{col 104}{space 3}  .230177
{txt}{space 49} {c |}
{space 29}distpreviouspartycmp {c |}{col 51}{res}{space 2}-.0322677{col 63}{space 2} .0281433{col 74}{space 1}   -1.15{col 83}{space 3}0.252{col 91}{space 4}-.0874275{col 104}{space 3} .0228921
{txt}{space 39}closeparty {c |}{col 51}{res}{space 2} .1387863{col 63}{space 2} .0630146{col 74}{space 1}    2.20{col 83}{space 3}0.028{col 91}{space 4}   .01528{col 104}{space 3} .2622926
{txt}{space 37}p_government {c |}{col 51}{res}{space 2}-.4087942{col 63}{space 2}  .337131{col 74}{space 1}   -1.21{col 83}{space 3}0.225{col 91}{space 4}-1.069559{col 104}{space 3} .2519705
{txt}{space 42}sd_rile {c |}{col 51}{res}{space 2}   .00703{col 63}{space 2} .0186334{col 74}{space 1}    0.38{col 83}{space 3}0.706{col 91}{space 4}-.0294908{col 104}{space 3} .0435508
{txt}{space 23}lvotetotradicallr_combined {c |}{col 51}{res}{space 2} .0196133{col 63}{space 2} .0142759{col 74}{space 1}    1.37{col 83}{space 3}0.169{col 91}{space 4} -.008367{col 104}{space 3} .0475936
{txt}{space 29}lcorporatism_z_sm537 {c |}{col 51}{res}{space 2} 1.364873{col 63}{space 2}  .414295{col 74}{space 1}    3.29{col 83}{space 3}0.001{col 91}{space 4} .5528699{col 104}{space 3} 2.176876
{txt}{space 44}_cons {c |}{col 51}{res}{space 2}-.7778874{col 63}{space 2} .4503028{col 74}{space 1}   -1.73{col 83}{space 3}0.084{col 91}{space 4}-1.660465{col 104}{space 3} .1046899
{txt}{hline 50}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 39}/athrho2_1 {c |}{col 51}{res}{space 2}-.0349081{col 63}{space 2} .1422917{col 74}{space 1}   -0.25{col 83}{space 3}0.806{col 91}{space 4}-.3137947{col 104}{space 3} .2439785
{txt}{space 40}/lnsigma2 {c |}{col 51}{res}{space 2}-.3901821{col 63}{space 2} .2041426{col 74}{space 1}   -1.91{col 83}{space 3}0.056{col 91}{space 4}-.7902942{col 104}{space 3} .0099301
{txt}{hline 50}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
 corr(e.lpss_mod3_upd,e.c_mainstream_vs_radicalrl){c |}{col 51}{res}{space 2}-.0348939{col 63}{space 2} .1421184{col 91}{space 4}-.3038853{col 104}{space 3} .2392501
{txt}                               sd(e.lpss_mod3_upd){c |}{col 51}{res}{space 2} .6769336{col 63}{space 2}  .138191{col 91}{space 4} .4537113{col 104}{space 3}  1.00998
{txt}{hline 50}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of exogeneity (corr = 0): chi2({res}1{txt}) = {res}0.06{txt}{col 59}Prob > chi2 = {res}0.8062
{txt}{p 0 14 68}Instrumented: {res:lpss_mod3_upd}{p_end}
{p 0 14 68}{space 1}Instruments: {res:male age 2.highedu 3.highedu dissatisfied 2.income_3cat 3.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotradicallr_combined lcorporatism_z_sm537}{p_end}

{com}. //t=3.29
. display 3.29^2
{res}10.8241
{txt}
{com}. est store M4
{txt}
{com}. ivprobit c_green_vs_mainstream male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined (lpss_mod3_upd=lcorporatism_z_sm537) if p_green_vs_mainstream==0, first vce(cluster country_elec)
{res}
{txt}Fitting exogenous probit model

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2630.5471}  
Iteration 1:{space 3}log likelihood = {res:-2263.7669}  
Iteration 2:{space 3}log likelihood = {res:-2212.3093}  
Iteration 3:{space 3}log likelihood = {res:-2211.3912}  
Iteration 4:{space 3}log likelihood = {res:-2211.3908}  
Iteration 5:{space 3}log likelihood = {res:-2211.3908}  
{res}
{txt}Fitting full model

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-26837.008}  
Iteration 1:{space 3}log pseudolikelihood = {res:-26837.006}  

{col 1}Probit model with endogenous regressors{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:23,636}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:224.28}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-26837.006}{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}

{txt}{ralign 111:(Std. err. adjusted for {res:38} clusters in {res:country_elec})}
{hline 46}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 47}{c |}{col 59}    Robust
{col 47}{c |} Coefficient{col 59}  std. err.{col 71}      z{col 79}   P>|z|{col 87}     [95% con{col 100}f. interval]
{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}c_green_vs_mainstream                         {txt}{c |}
{space 32}lpss_mod3_upd {c |}{col 47}{res}{space 2}-.0762734{col 59}{space 2} .0711954{col 70}{space 1}   -1.07{col 79}{space 3}0.284{col 87}{space 4}-.2158138{col 100}{space 3} .0632669
{txt}{space 41}male {c |}{col 47}{res}{space 2}-.1517607{col 59}{space 2} .0336051{col 70}{space 1}   -4.52{col 79}{space 3}0.000{col 87}{space 4}-.2176256{col 100}{space 3}-.0858959
{txt}{space 42}age {c |}{col 47}{res}{space 2}-.0091853{col 59}{space 2} .0018509{col 70}{space 1}   -4.96{col 79}{space 3}0.000{col 87}{space 4} -.012813{col 100}{space 3}-.0055576
{txt}{space 45} {c |}
{space 38}highedu {c |}
{space 25}Secondary education  {c |}{col 47}{res}{space 2} .1946294{col 59}{space 2} .1239592{col 70}{space 1}    1.57{col 79}{space 3}0.116{col 87}{space 4}-.0483261{col 100}{space 3} .4375849
{txt}{space 20}Post-secondary education  {c |}{col 47}{res}{space 2} .4484358{col 59}{space 2} .1441625{col 70}{space 1}    3.11{col 79}{space 3}0.002{col 87}{space 4} .1658825{col 100}{space 3}  .730989
{txt}{space 45} {c |}
{space 33}dissatisfied {c |}{col 47}{res}{space 2} .0309874{col 59}{space 2} .0565254{col 70}{space 1}    0.55{col 79}{space 3}0.584{col 87}{space 4}-.0798005{col 100}{space 3} .1417752
{txt}{space 45} {c |}
{space 34}income_3cat {c |}
{space 31}Medium income  {c |}{col 47}{res}{space 2} .0101459{col 59}{space 2} .0602977{col 70}{space 1}    0.17{col 79}{space 3}0.866{col 87}{space 4}-.1080354{col 100}{space 3} .1283273
{txt}{space 33}High income  {c |}{col 47}{res}{space 2}-.0563687{col 59}{space 2}  .086652{col 70}{space 1}   -0.65{col 79}{space 3}0.515{col 87}{space 4}-.2262034{col 100}{space 3} .1134661
{txt}{space 45} {c |}
{space 25}distpreviouspartycmp {c |}{col 47}{res}{space 2}-.0395336{col 59}{space 2} .0216722{col 70}{space 1}   -1.82{col 79}{space 3}0.068{col 87}{space 4}-.0820104{col 100}{space 3} .0029432
{txt}{space 35}closeparty {c |}{col 47}{res}{space 2}-.2833452{col 59}{space 2} .0482476{col 70}{space 1}   -5.87{col 79}{space 3}0.000{col 87}{space 4}-.3779089{col 100}{space 3}-.1887816
{txt}{space 33}p_government {c |}{col 47}{res}{space 2} .1723071{col 59}{space 2} .1148367{col 70}{space 1}    1.50{col 79}{space 3}0.133{col 87}{space 4}-.0527686{col 100}{space 3} .3973828
{txt}{space 38}sd_rile {c |}{col 47}{res}{space 2}-.0044863{col 59}{space 2} .0068449{col 70}{space 1}   -0.66{col 79}{space 3}0.512{col 87}{space 4}-.0179021{col 100}{space 3} .0089295
{txt}{space 23}lvotetotgreen_combined {c |}{col 47}{res}{space 2} .1383443{col 59}{space 2} .0161725{col 70}{space 1}    8.55{col 79}{space 3}0.000{col 87}{space 4} .1066467{col 100}{space 3} .1700418
{txt}{space 40}_cons {c |}{col 47}{res}{space 2}-2.392137{col 59}{space 2} .2149756{col 70}{space 1}  -11.13{col 79}{space 3}0.000{col 87}{space 4}-2.813482{col 100}{space 3}-1.970793
{txt}{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lpss_mod3_upd                                 {txt}{c |}
{space 41}male {c |}{col 47}{res}{space 2}-.0044551{col 59}{space 2} .0111647{col 70}{space 1}   -0.40{col 79}{space 3}0.690{col 87}{space 4}-.0263375{col 100}{space 3} .0174273
{txt}{space 42}age {c |}{col 47}{res}{space 2} .0020275{col 59}{space 2}  .001574{col 70}{space 1}    1.29{col 79}{space 3}0.198{col 87}{space 4}-.0010574{col 100}{space 3} .0051124
{txt}{space 45} {c |}
{space 38}highedu {c |}
{space 25}Secondary education  {c |}{col 47}{res}{space 2} .1543211{col 59}{space 2} .0946031{col 70}{space 1}    1.63{col 79}{space 3}0.103{col 87}{space 4}-.0310977{col 100}{space 3} .3397398
{txt}{space 20}Post-secondary education  {c |}{col 47}{res}{space 2} .0603026{col 59}{space 2} .1257579{col 70}{space 1}    0.48{col 79}{space 3}0.632{col 87}{space 4}-.1861783{col 100}{space 3} .3067835
{txt}{space 45} {c |}
{space 33}dissatisfied {c |}{col 47}{res}{space 2} .0219732{col 59}{space 2} .0540062{col 70}{space 1}    0.41{col 79}{space 3}0.684{col 87}{space 4}-.0838769{col 100}{space 3} .1278233
{txt}{space 45} {c |}
{space 34}income_3cat {c |}
{space 31}Medium income  {c |}{col 47}{res}{space 2} .0089509{col 59}{space 2} .0249497{col 70}{space 1}    0.36{col 79}{space 3}0.720{col 87}{space 4}-.0399497{col 100}{space 3} .0578516
{txt}{space 33}High income  {c |}{col 47}{res}{space 2} .0585954{col 59}{space 2} .0613265{col 70}{space 1}    0.96{col 79}{space 3}0.339{col 87}{space 4}-.0616023{col 100}{space 3}  .178793
{txt}{space 45} {c |}
{space 25}distpreviouspartycmp {c |}{col 47}{res}{space 2}-.0086945{col 59}{space 2}  .026781{col 70}{space 1}   -0.32{col 79}{space 3}0.745{col 87}{space 4}-.0611842{col 100}{space 3} .0437953
{txt}{space 35}closeparty {c |}{col 47}{res}{space 2} .0004238{col 59}{space 2} .0568654{col 70}{space 1}    0.01{col 79}{space 3}0.994{col 87}{space 4}-.1110303{col 100}{space 3} .1118778
{txt}{space 33}p_government {c |}{col 47}{res}{space 2} .0221737{col 59}{space 2} .0855027{col 70}{space 1}    0.26{col 79}{space 3}0.795{col 87}{space 4}-.1454084{col 100}{space 3} .1897559
{txt}{space 38}sd_rile {c |}{col 47}{res}{space 2}  .006479{col 59}{space 2} .0139626{col 70}{space 1}    0.46{col 79}{space 3}0.643{col 87}{space 4}-.0208872{col 100}{space 3} .0338452
{txt}{space 23}lvotetotgreen_combined {c |}{col 47}{res}{space 2}-.0859661{col 59}{space 2} .0297429{col 70}{space 1}   -2.89{col 79}{space 3}0.004{col 87}{space 4} -.144261{col 100}{space 3}-.0276711
{txt}{space 25}lcorporatism_z_sm537 {c |}{col 47}{res}{space 2} 1.985874{col 59}{space 2}   .44594{col 70}{space 1}    4.45{col 79}{space 3}0.000{col 87}{space 4} 1.111847{col 100}{space 3}   2.8599
{txt}{space 40}_cons {c |}{col 47}{res}{space 2}-.7831153{col 59}{space 2} .3355657{col 70}{space 1}   -2.33{col 79}{space 3}0.020{col 87}{space 4}-1.440812{col 100}{space 3}-.1254187
{txt}{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 35}/athrho2_1 {c |}{col 47}{res}{space 2} .0285346{col 59}{space 2} .0605604{col 70}{space 1}    0.47{col 79}{space 3}0.638{col 87}{space 4}-.0901615{col 100}{space 3} .1472308
{txt}{space 36}/lnsigma2 {c |}{col 47}{res}{space 2}-.3770697{col 59}{space 2} .1332242{col 70}{space 1}   -2.83{col 79}{space 3}0.005{col 87}{space 4}-.6381843{col 100}{space 3} -.115955
{txt}{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
 corr(e.lpss_mod3_upd,e.c_green_vs_mainstream){c |}{col 47}{res}{space 2} .0285269{col 59}{space 2} .0605111{col 87}{space 4} -.089918{col 100}{space 3} .1461761
{txt}                           sd(e.lpss_mod3_upd){c |}{col 47}{res}{space 2} .6858683{col 59}{space 2} .0913743{col 87}{space 4} .5282507{col 100}{space 3} .8905153
{txt}{hline 46}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of exogeneity (corr = 0): chi2({res}1{txt}) = {res}0.22{txt}{col 59}Prob > chi2 = {res}0.6375
{txt}{p 0 14 72}Instrumented: {res:lpss_mod3_upd}{p_end}
{p 0 14 72}{space 1}Instruments: {res:male age 2.highedu 3.highedu dissatisfied 2.income_3cat 3.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lcorporatism_z_sm537}{p_end}

{com}. //t=4.45
. display 4.45^2
{res}19.8025
{txt}
{com}. est store M5
{txt}
{com}. ivprobit c_mainstream_vs_green male age i.highedu dissatisfied i.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined (lpss_mod3_upd=lcorporatism_z_sm537) if p_green_vs_mainstream==1, first vce(cluster country_elec)
{res}
{txt}Fitting exogenous probit model

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-607.18396}  
Iteration 1:{space 3}log likelihood = {res:-585.48952}  
Iteration 2:{space 3}log likelihood = {res:-585.44571}  
Iteration 3:{space 3}log likelihood = {res:-585.44571}  
{res}
{txt}Fitting full model

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1567.3206}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1567.2726}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1567.2726}  

{col 1}Probit model with endogenous regressors{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,032}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:145.97}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1567.2726}{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}

{txt}{ralign 111:(Std. err. adjusted for {res:20} clusters in {res:country_elec})}
{hline 46}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 47}{c |}{col 59}    Robust
{col 47}{c |} Coefficient{col 59}  std. err.{col 71}      z{col 79}   P>|z|{col 87}     [95% con{col 100}f. interval]
{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}c_mainstream_vs_green                         {txt}{c |}
{space 32}lpss_mod3_upd {c |}{col 47}{res}{space 2}  .008613{col 59}{space 2} .0346389{col 70}{space 1}    0.25{col 79}{space 3}0.804{col 87}{space 4} -.059278{col 100}{space 3} .0765039
{txt}{space 41}male {c |}{col 47}{res}{space 2}-.1100259{col 59}{space 2} .0755992{col 70}{space 1}   -1.46{col 79}{space 3}0.146{col 87}{space 4}-.2581976{col 100}{space 3} .0381458
{txt}{space 42}age {c |}{col 47}{res}{space 2} .0001205{col 59}{space 2} .0037654{col 70}{space 1}    0.03{col 79}{space 3}0.974{col 87}{space 4}-.0072595{col 100}{space 3} .0075005
{txt}{space 45} {c |}
{space 38}highedu {c |}
{space 25}Secondary education  {c |}{col 47}{res}{space 2}-.0670808{col 59}{space 2} .1798621{col 70}{space 1}   -0.37{col 79}{space 3}0.709{col 87}{space 4} -.419604{col 100}{space 3} .2854424
{txt}{space 20}Post-secondary education  {c |}{col 47}{res}{space 2}-.1405706{col 59}{space 2} .2114605{col 70}{space 1}   -0.66{col 79}{space 3}0.506{col 87}{space 4}-.5550255{col 100}{space 3} .2738843
{txt}{space 45} {c |}
{space 33}dissatisfied {c |}{col 47}{res}{space 2} .0798989{col 59}{space 2} .0956081{col 70}{space 1}    0.84{col 79}{space 3}0.403{col 87}{space 4}-.1074895{col 100}{space 3} .2672872
{txt}{space 45} {c |}
{space 34}income_3cat {c |}
{space 31}Medium income  {c |}{col 47}{res}{space 2}-.0343508{col 59}{space 2} .0807221{col 70}{space 1}   -0.43{col 79}{space 3}0.670{col 87}{space 4}-.1925632{col 100}{space 3} .1238615
{txt}{space 33}High income  {c |}{col 47}{res}{space 2} .0181485{col 59}{space 2} .1113265{col 70}{space 1}    0.16{col 79}{space 3}0.871{col 87}{space 4}-.2000475{col 100}{space 3} .2363445
{txt}{space 45} {c |}
{space 25}distpreviouspartycmp {c |}{col 47}{res}{space 2} .1374494{col 59}{space 2} .0370816{col 70}{space 1}    3.71{col 79}{space 3}0.000{col 87}{space 4} .0647707{col 100}{space 3}  .210128
{txt}{space 35}closeparty {c |}{col 47}{res}{space 2}-.3624074{col 59}{space 2} .1050634{col 70}{space 1}   -3.45{col 79}{space 3}0.001{col 87}{space 4}-.5683278{col 100}{space 3}-.1564869
{txt}{space 33}p_government {c |}{col 47}{res}{space 2}-.0240101{col 59}{space 2} .1122055{col 70}{space 1}   -0.21{col 79}{space 3}0.831{col 87}{space 4}-.2439289{col 100}{space 3} .1959086
{txt}{space 38}sd_rile {c |}{col 47}{res}{space 2} .0038122{col 59}{space 2} .0066716{col 70}{space 1}    0.57{col 79}{space 3}0.568{col 87}{space 4}-.0092638{col 100}{space 3} .0168883
{txt}{space 23}lvotetotgreen_combined {c |}{col 47}{res}{space 2}-.0262076{col 59}{space 2} .0167432{col 70}{space 1}   -1.57{col 79}{space 3}0.118{col 87}{space 4}-.0590237{col 100}{space 3} .0066084
{txt}{space 40}_cons {c |}{col 47}{res}{space 2}-.3766304{col 59}{space 2} .2512444{col 70}{space 1}   -1.50{col 79}{space 3}0.134{col 87}{space 4}-.8690605{col 100}{space 3} .1157996
{txt}{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}lpss_mod3_upd                                 {txt}{c |}
{space 41}male {c |}{col 47}{res}{space 2}  .022744{col 59}{space 2} .0381382{col 70}{space 1}    0.60{col 79}{space 3}0.551{col 87}{space 4}-.0520056{col 100}{space 3} .0974936
{txt}{space 42}age {c |}{col 47}{res}{space 2} .0000241{col 59}{space 2} .0018916{col 70}{space 1}    0.01{col 79}{space 3}0.990{col 87}{space 4}-.0036834{col 100}{space 3} .0037316
{txt}{space 45} {c |}
{space 38}highedu {c |}
{space 25}Secondary education  {c |}{col 47}{res}{space 2}  .213596{col 59}{space 2} .1777442{col 70}{space 1}    1.20{col 79}{space 3}0.229{col 87}{space 4}-.1347762{col 100}{space 3} .5619682
{txt}{space 20}Post-secondary education  {c |}{col 47}{res}{space 2}-.0515869{col 59}{space 2} .1029431{col 70}{space 1}   -0.50{col 79}{space 3}0.616{col 87}{space 4}-.2533518{col 100}{space 3} .1501779
{txt}{space 45} {c |}
{space 33}dissatisfied {c |}{col 47}{res}{space 2} .0549635{col 59}{space 2} .0852782{col 70}{space 1}    0.64{col 79}{space 3}0.519{col 87}{space 4}-.1121786{col 100}{space 3} .2221057
{txt}{space 45} {c |}
{space 34}income_3cat {c |}
{space 31}Medium income  {c |}{col 47}{res}{space 2} .0731921{col 59}{space 2}  .089996{col 70}{space 1}    0.81{col 79}{space 3}0.416{col 87}{space 4}-.1031967{col 100}{space 3}  .249581
{txt}{space 33}High income  {c |}{col 47}{res}{space 2} .1896602{col 59}{space 2} .1431583{col 70}{space 1}    1.32{col 79}{space 3}0.185{col 87}{space 4} -.090925{col 100}{space 3} .4702453
{txt}{space 45} {c |}
{space 25}distpreviouspartycmp {c |}{col 47}{res}{space 2}-.0148093{col 59}{space 2} .0288896{col 70}{space 1}   -0.51{col 79}{space 3}0.608{col 87}{space 4}-.0714318{col 100}{space 3} .0418132
{txt}{space 35}closeparty {c |}{col 47}{res}{space 2} .0624538{col 59}{space 2} .0418791{col 70}{space 1}    1.49{col 79}{space 3}0.136{col 87}{space 4}-.0196276{col 100}{space 3} .1445353
{txt}{space 33}p_government {c |}{col 47}{res}{space 2} .1228573{col 59}{space 2} .3950247{col 70}{space 1}    0.31{col 79}{space 3}0.756{col 87}{space 4}-.6513769{col 100}{space 3} .8970915
{txt}{space 38}sd_rile {c |}{col 47}{res}{space 2} .0167856{col 59}{space 2} .0234583{col 70}{space 1}    0.72{col 79}{space 3}0.474{col 87}{space 4}-.0291918{col 100}{space 3}  .062763
{txt}{space 23}lvotetotgreen_combined {c |}{col 47}{res}{space 2} .0413368{col 59}{space 2} .1079453{col 70}{space 1}    0.38{col 79}{space 3}0.702{col 87}{space 4}-.1702321{col 100}{space 3} .2529056
{txt}{space 25}lcorporatism_z_sm537 {c |}{col 47}{res}{space 2} 2.663766{col 59}{space 2} .4324601{col 70}{space 1}    6.16{col 79}{space 3}0.000{col 87}{space 4}  1.81616{col 100}{space 3} 3.511372
{txt}{space 40}_cons {c |}{col 47}{res}{space 2}-2.034279{col 59}{space 2} .5549267{col 70}{space 1}   -3.67{col 79}{space 3}0.000{col 87}{space 4}-3.121915{col 100}{space 3}-.9466426
{txt}{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 35}/athrho2_1 {c |}{col 47}{res}{space 2} .0311075{col 59}{space 2} .0306127{col 70}{space 1}    1.02{col 79}{space 3}0.310{col 87}{space 4}-.0288923{col 100}{space 3} .0911073
{txt}{space 36}/lnsigma2 {c |}{col 47}{res}{space 2}-.4675559{col 59}{space 2} .2159596{col 70}{space 1}   -2.17{col 79}{space 3}0.030{col 87}{space 4} -.890829{col 100}{space 3}-.0442827
{txt}{hline 46}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
 corr(e.lpss_mod3_upd,e.c_mainstream_vs_green){c |}{col 47}{res}{space 2} .0310975{col 59}{space 2} .0305831{col 87}{space 4}-.0288842{col 100}{space 3}  .090856
{txt}                           sd(e.lpss_mod3_upd){c |}{col 47}{res}{space 2} .6265317{col 59}{space 2} .1353056{col 87}{space 4} .4103155{col 100}{space 3} .9566834
{txt}{hline 46}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of exogeneity (corr = 0): chi2({res}1{txt}) = {res}1.03{txt}{col 59}Prob > chi2 = {res}0.3096
{txt}{p 0 14 72}Instrumented: {res:lpss_mod3_upd}{p_end}
{p 0 14 72}{space 1}Instruments: {res:male age 2.highedu 3.highedu dissatisfied 2.income_3cat 3.income_3cat distpreviouspartycmp closeparty p_government sd_rile lvotetotgreen_combined lcorporatism_z_sm537}{p_end}

{com}. //t=6.16
. display 6.16^2
{res}37.9456
{txt}
{com}. est store M6
{txt}
{com}. 
. esttab M1 M2 M3 M4 M5 M6 using "tablea20.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") title(Table A20. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1 1 1 1 1 1) replace
{res}{txt}(output written to {browse  `"tablea20.rtf"'})

{com}. 
. *************
. **Table A21**
. *************
. 
. mlogit c_radmainvsniche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_radicalrl_vs_mainstream==0 ///
> , baseoutcome(3) vce(cluster country_elec)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-19535.302}  
Iteration 1:{space 3}log pseudolikelihood = {res:-18751.168}  
Iteration 2:{space 3}log pseudolikelihood = {res: -18690.13}  
Iteration 3:{space 3}log pseudolikelihood = {res:-18689.846}  
Iteration 4:{space 3}log pseudolikelihood = {res:-18689.846}  
{res}
{txt}{col 1}Multinomial logistic regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:24,990}
{txt}{col 56}{lalign 13:Wald chi2({res:26})}{col 69} = {res}{ralign 7:2271.32}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-18689.846}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0433}

{txt}{ralign 94:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}            c_radmainvsniche{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      z{col 62}   P>|z|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Radical_party                {txt}{c |}
{space 24}male {c |}{col 30}{res}{space 2} .2817276{col 42}{space 2} .0673378{col 53}{space 1}    4.18{col 62}{space 3}0.000{col 70}{space 4} .1497479{col 83}{space 3} .4137073
{txt}{space 25}age {c |}{col 30}{res}{space 2}-.0060042{col 42}{space 2} .0034607{col 53}{space 1}   -1.73{col 62}{space 3}0.083{col 70}{space 4} -.012787{col 83}{space 3} .0007786
{txt}{space 28} {c |}
{space 21}highedu {c |}
{space 8}Secondary education  {c |}{col 30}{res}{space 2} 1.001534{col 42}{space 2} .2192038{col 53}{space 1}    4.57{col 62}{space 3}0.000{col 70}{space 4} .5719021{col 83}{space 3} 1.431165
{txt}{space 3}Post-secondary education  {c |}{col 30}{res}{space 2} 1.073359{col 42}{space 2} .2227588{col 53}{space 1}    4.82{col 62}{space 3}0.000{col 70}{space 4} .6367596{col 83}{space 3} 1.509958
{txt}{space 28} {c |}
{space 17}income_3cat {c |}
{space 14}Medium income  {c |}{col 30}{res}{space 2}-.3396915{col 42}{space 2} .0795146{col 53}{space 1}   -4.27{col 62}{space 3}0.000{col 70}{space 4}-.4955373{col 83}{space 3}-.1838457
{txt}{space 16}High income  {c |}{col 30}{res}{space 2}-.8844347{col 42}{space 2} .1537242{col 53}{space 1}   -5.75{col 62}{space 3}0.000{col 70}{space 4}-1.185729{col 83}{space 3}-.5831408
{txt}{space 28} {c |}
{space 16}dissatisfied {c |}{col 30}{res}{space 2} .8887338{col 42}{space 2}  .139291{col 53}{space 1}    6.38{col 62}{space 3}0.000{col 70}{space 4} .6157284{col 83}{space 3} 1.161739
{txt}{space 8}distpreviouspartycmp {c |}{col 30}{res}{space 2} .0117781{col 42}{space 2} .0421262{col 53}{space 1}    0.28{col 62}{space 3}0.780{col 70}{space 4}-.0707878{col 83}{space 3}  .094344
{txt}{space 18}closeparty {c |}{col 30}{res}{space 2}-.9826181{col 42}{space 2}  .150096{col 53}{space 1}   -6.55{col 62}{space 3}0.000{col 70}{space 4}-1.276801{col 83}{space 3}-.6884354
{txt}{space 16}p_government {c |}{col 30}{res}{space 2} .2272494{col 42}{space 2} .2326238{col 53}{space 1}    0.98{col 62}{space 3}0.329{col 70}{space 4}-.2286849{col 83}{space 3} .6831836
{txt}{space 21}sd_rile {c |}{col 30}{res}{space 2} .0001361{col 42}{space 2} .0184981{col 53}{space 1}    0.01{col 62}{space 3}0.994{col 70}{space 4}-.0361195{col 83}{space 3} .0363918
{txt}{space 6}lvotetotniche_combined {c |}{col 30}{res}{space 2} .0132563{col 42}{space 2} .0110996{col 53}{space 1}    1.19{col 62}{space 3}0.232{col 70}{space 4}-.0084986{col 83}{space 3} .0350112
{txt}{space 15}lpss_mod3_upd {c |}{col 30}{res}{space 2}  .309786{col 42}{space 2} .1962571{col 53}{space 1}    1.58{col 62}{space 3}0.114{col 70}{space 4}-.0748708{col 83}{space 3} .6944428
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-3.184252{col 42}{space 2} .5965803{col 53}{space 1}   -5.34{col 62}{space 3}0.000{col 70}{space 4}-4.353528{col 83}{space 3}-2.014976
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Radical_mainstream_party     {txt}{c |}
{space 24}male {c |}{col 30}{res}{space 2} .0549774{col 42}{space 2}  .047151{col 53}{space 1}    1.17{col 62}{space 3}0.244{col 70}{space 4}-.0374369{col 83}{space 3} .1473917
{txt}{space 25}age {c |}{col 30}{res}{space 2} .0021299{col 42}{space 2} .0029746{col 53}{space 1}    0.72{col 62}{space 3}0.474{col 70}{space 4}-.0037001{col 83}{space 3} .0079599
{txt}{space 28} {c |}
{space 21}highedu {c |}
{space 8}Secondary education  {c |}{col 30}{res}{space 2} .5004223{col 42}{space 2} .2059909{col 53}{space 1}    2.43{col 62}{space 3}0.015{col 70}{space 4} .0966876{col 83}{space 3} .9041571
{txt}{space 3}Post-secondary education  {c |}{col 30}{res}{space 2} .3746205{col 42}{space 2} .2342309{col 53}{space 1}    1.60{col 62}{space 3}0.110{col 70}{space 4}-.0844637{col 83}{space 3} .8337047
{txt}{space 28} {c |}
{space 17}income_3cat {c |}
{space 14}Medium income  {c |}{col 30}{res}{space 2}   .02465{col 42}{space 2} .0641251{col 53}{space 1}    0.38{col 62}{space 3}0.701{col 70}{space 4}-.1010329{col 83}{space 3} .1503329
{txt}{space 16}High income  {c |}{col 30}{res}{space 2}-.0769887{col 42}{space 2} .1173448{col 53}{space 1}   -0.66{col 62}{space 3}0.512{col 70}{space 4}-.3069802{col 83}{space 3} .1530029
{txt}{space 28} {c |}
{space 16}dissatisfied {c |}{col 30}{res}{space 2} .1816728{col 42}{space 2} .1662725{col 53}{space 1}    1.09{col 62}{space 3}0.275{col 70}{space 4}-.1442152{col 83}{space 3} .5075608
{txt}{space 8}distpreviouspartycmp {c |}{col 30}{res}{space 2} .0739518{col 42}{space 2} .0578423{col 53}{space 1}    1.28{col 62}{space 3}0.201{col 70}{space 4}-.0394169{col 83}{space 3} .1873206
{txt}{space 18}closeparty {c |}{col 30}{res}{space 2}  .019376{col 42}{space 2} .0909941{col 53}{space 1}    0.21{col 62}{space 3}0.831{col 70}{space 4}-.1589691{col 83}{space 3} .1977211
{txt}{space 16}p_government {c |}{col 30}{res}{space 2}  .118507{col 42}{space 2} .4631461{col 53}{space 1}    0.26{col 62}{space 3}0.798{col 70}{space 4}-.7892427{col 83}{space 3} 1.026257
{txt}{space 21}sd_rile {c |}{col 30}{res}{space 2} .0316836{col 42}{space 2} .0262398{col 53}{space 1}    1.21{col 62}{space 3}0.227{col 70}{space 4}-.0197454{col 83}{space 3} .0831126
{txt}{space 6}lvotetotniche_combined {c |}{col 30}{res}{space 2} .0054091{col 42}{space 2} .0167632{col 53}{space 1}    0.32{col 62}{space 3}0.747{col 70}{space 4}-.0274462{col 83}{space 3} .0382643
{txt}{space 15}lpss_mod3_upd {c |}{col 30}{res}{space 2}-.1386341{col 42}{space 2} .1605146{col 53}{space 1}   -0.86{col 62}{space 3}0.388{col 70}{space 4} -.453237{col 83}{space 3} .1759688
{txt}{space 23}_cons {c |}{col 30}{res}{space 2}-2.296387{col 42}{space 2} .6572383{col 53}{space 1}   -3.49{col 62}{space 3}0.000{col 70}{space 4} -3.58455{col 83}{space 3}-1.008223
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Non_radical_mainstream_party{col 30}{txt}{c |}  (base outcome)
{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store M1
{txt}
{com}. estadd scalar N_elections = e(N_clust)

{txt}added scalar:
        e(N_elections) =  {res}39
{txt}
{com}. 
. 
. esttab M1 using "tablea21.rtf", se aic bic starlevels(+ 0.10 * 0.05 ** 0.01 *** 0.001) mtitles("Main-->Niche" "Niche-->Main" "Main-->Radical" "Radical-->Main" "Main-->Green" "Green-->Main") title(Table A21. Unstandardized models (odds ratios reported)) legend label collabels(none) varlabels(_cons Constant) eform(1) replace
{res}{txt}(output written to {browse  `"tablea21.rtf"'})

{com}. 
. ************
. **Figure 5**
. ************
. 
. preserve
{txt}
{com}. 
. use "agreementscores_long.dta", clear
{txt}( )

{com}. 
. rename agr_long agr
{res}{txt}
{com}. 
. drop if agr==.
{txt}(101,070 observations deleted)

{com}. bys resid: gen n=_n
{txt}
{com}. keep if n==1
{txt}(80,010 observations deleted)

{com}. 
. reg agr lpss_mod3_upd age male highedu closeparty

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    22,635
{txt}{hline 13}{c +}{hline 34}   F(5, 22629)     = {res}   301.53
{txt}       Model {c |} {res} 112.387079         5  22.4774158   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1686.85088    22,629  .074543766   {txt}R-squared       ={res}    0.0625
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0623
{txt}       Total {c |} {res} 1799.23796    22,634  .079492708   {txt}Root MSE        =   {res} .27303

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          agr{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
lpss_mod3_upd {c |}{col 15}{res}{space 2} .0523136{col 27}{space 2} .0016503{col 38}{space 1}   31.70{col 47}{space 3}0.000{col 55}{space 4} .0490788{col 68}{space 3} .0555484
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0001239{col 27}{space 2} .0001217{col 38}{space 1}    1.02{col 47}{space 3}0.309{col 55}{space 4}-.0001146{col 68}{space 3} .0003624
{txt}{space 9}male {c |}{col 15}{res}{space 2} .0326974{col 27}{space 2} .0036391{col 38}{space 1}    8.98{col 47}{space 3}0.000{col 55}{space 4} .0255645{col 68}{space 3} .0398304
{txt}{space 6}highedu {c |}{col 15}{res}{space 2} .0283424{col 27}{space 2} .0027506{col 38}{space 1}   10.30{col 47}{space 3}0.000{col 55}{space 4}  .022951{col 68}{space 3} .0337339
{txt}{space 3}closeparty {c |}{col 15}{res}{space 2}-.0624093{col 27}{space 2} .0036779{col 38}{space 1}  -16.97{col 47}{space 3}0.000{col 55}{space 4}-.0696183{col 68}{space 3}-.0552003
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .4066121{col 27}{space 2} .0103204{col 38}{space 1}   39.40{col 47}{space 3}0.000{col 55}{space 4} .3863833{col 68}{space 3} .4268408
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. margins, at(lpss_mod3_upd=(-4(0.5)2.5)) vsquish post
{res}
{txt}{col 1}Predictive margins{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:22,635}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-4}}
{lalign 8:2._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3.5}}
{lalign 8:3._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3}}
{lalign 8:4._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2.5}}
{lalign 8:5._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2}}
{lalign 8:6._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1.5}}
{lalign 8:7._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1}}
{lalign 8:8._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-.5}}
{lalign 8:9._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:0}}
{lalign 8:10._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:.5}}
{lalign 8:11._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1}}
{lalign 8:12._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1.5}}
{lalign 8:13._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2}}
{lalign 8:14._at: }{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2.5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2547302{col 26}{space 2} .0065906{col 37}{space 1}   38.65{col 46}{space 3}0.000{col 54}{space 4} .2418122{col 67}{space 3} .2676481
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .280887{col 26}{space 2} .0058017{col 37}{space 1}   48.41{col 46}{space 3}0.000{col 54}{space 4} .2695152{col 67}{space 3} .2922588
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3070438{col 26}{space 2} .0050246{col 37}{space 1}   61.11{col 46}{space 3}0.000{col 54}{space 4} .2971952{col 67}{space 3} .3168923
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3332006{col 26}{space 2} .0042656{col 37}{space 1}   78.11{col 46}{space 3}0.000{col 54}{space 4} .3248398{col 67}{space 3} .3415614
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .3593574{col 26}{space 2} .0035363{col 37}{space 1}  101.62{col 46}{space 3}0.000{col 54}{space 4} .3524261{col 67}{space 3} .3662887
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .3855142{col 26}{space 2} .0028596{col 37}{space 1}  134.82{col 46}{space 3}0.000{col 54}{space 4} .3799093{col 67}{space 3} .3911191
{txt}{space 10}7  {c |}{col 14}{res}{space 2}  .411671{col 26}{space 2} .0022827{col 37}{space 1}  180.34{col 46}{space 3}0.000{col 54}{space 4} .4071967{col 67}{space 3} .4161453
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .4378278{col 26}{space 2} .0018991{col 37}{space 1}  230.55{col 46}{space 3}0.000{col 54}{space 4} .4341056{col 67}{space 3} .4415501
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .4639847{col 26}{space 2} .0018341{col 37}{space 1}  252.98{col 46}{space 3}0.000{col 54}{space 4} .4603897{col 67}{space 3} .4675796
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .4901415{col 26}{space 2} .0021173{col 37}{space 1}  231.49{col 46}{space 3}0.000{col 54}{space 4} .4859914{col 67}{space 3} .4942916
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .5162983{col 26}{space 2}  .002639{col 37}{space 1}  195.64{col 46}{space 3}0.000{col 54}{space 4} .5111257{col 67}{space 3} .5214708
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5424551{col 26}{space 2} .0032874{col 37}{space 1}  165.01{col 46}{space 3}0.000{col 54}{space 4} .5360116{col 67}{space 3} .5488986
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5686119{col 26}{space 2} .0040014{col 37}{space 1}  142.10{col 46}{space 3}0.000{col 54}{space 4} .5607688{col 67}{space 3}  .576455
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5947687{col 26}{space 2} .0047516{col 37}{space 1}  125.17{col 46}{space 3}0.000{col 54}{space 4} .5854552{col 67}{space 3} .6040823
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix y_=r(table)'
{txt}
{com}. svmat y_
{txt}
{com}. range g -4 2.5 14
{txt}(22,621 missing values generated)

{com}. 
. twoway hist lpss_mod3_upd, percent yaxis(2) ///
> || line y_5 g, lcolor(black) lpattern(dash dash) ///
> || line y_6 g, lcolor(black) lpattern(dash dash) ///
> || line y_1 g, lcolor(black) lpattern(solid) ///
> , xlabel(-4(0.5)2.5) xtitle("Party system saturation t-1", size(medsmall)) ///
> yscale(alt) yscale(alt axis(2)) ///
> ytitle("% of observations", size(medsmall) axis(2)) ///
> ytitle("Perceived agreement positions mainstream parties", size(medsmall) axis(1)) ///
> legend(off) ///
> scheme(plotplain) name(a, replace)
{res}{txt}
{com}. 
. restore
{txt}
{com}. 
. preserve
{txt}
{com}. 
. capture drop sample sample2 inanalysis
{txt}
{com}. 
. bys ccode (alpha_3) : replace alpha_3 = alpha_3[_N]
{txt}(0 real changes made)

{com}. assert alpha_3!=""
{txt}
{com}. 
. //Merge information on whether CSES party a-i are radical or mainstream
. sort alpha_3 elec_year elec_month
{txt}
{com}. merge alpha_3 elec_year elec_month using "partyaf_mainniche.dta", sort uniqus
{txt}{p}
(you are using old
{bf:merge} syntax; see
{bf:{help merge:[D] merge}} for new syntax)
{p_end}
{p 0 4 2}
variable{txt}s{txt} alpha_3
elec_year
elec_month
do not uniquely identify observations in
the master data
{p_end}

{com}. keep if _merge==3
{txt}(0 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. melogit c_niche male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_niche==0 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -7330.739}  
Iteration 1:{space 3}log likelihood = {res:-6961.3936}  
Iteration 2:{space 3}log likelihood = {res:-6959.7544}  
Iteration 3:{space 3}log likelihood = {res:-6959.7524}  
Iteration 4:{space 3}log likelihood = {res:-6959.7524}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-6798.9628}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6798.9628}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-6794.7893}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6784.2505}  
Iteration 3:{space 3}log pseudolikelihood = {res:-6773.1835}  
Iteration 4:{space 3}log pseudolikelihood = {res:-6773.1452}  
Iteration 5:{space 3}log pseudolikelihood = {res:-6773.1451}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    25,872
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        39

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        80
{col 63}{txt}avg{col 67}={res}{col 69}     663.4
{col 63}{txt}max{col 67}={res}{col 69}     1,296

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   444.02
{txt}Log pseudolikelihood = {res}-6773.1451{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:39} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  c_niche{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} 1.085267{col 39}{space 2}  .069409{col 50}{space 1}    1.28{col 59}{space 3}0.201{col 67}{space 4} .9574088{col 80}{space 3}   1.2302
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9845907{col 39}{space 2}  .002532{col 50}{space 1}   -6.04{col 59}{space 3}0.000{col 67}{space 4} .9796406{col 80}{space 3} .9895658
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.273162{col 39}{space 2} .1216711{col 50}{space 1}    2.53{col 59}{space 3}0.012{col 67}{space 4} 1.055693{col 80}{space 3} 1.535428
{txt}Post-secondary education  {c |}{col 27}{res}{space 2}  1.37776{col 39}{space 2} .1755047{col 50}{space 1}    2.52{col 59}{space 3}0.012{col 67}{space 4} 1.073357{col 80}{space 3} 1.768492
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} .8141882{col 39}{space 2} .0557074{col 50}{space 1}   -3.00{col 59}{space 3}0.003{col 67}{space 4}  .712008{col 80}{space 3} .9310322
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} .6456305{col 39}{space 2} .0596001{col 50}{space 1}   -4.74{col 59}{space 3}0.000{col 67}{space 4} .5387745{col 80}{space 3} .7736793
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} 2.099625{col 39}{space 2} .1766009{col 50}{space 1}    8.82{col 59}{space 3}0.000{col 67}{space 4} 1.780519{col 80}{space 3} 2.475922
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2} .9736146{col 39}{space 2} .0341088{col 50}{space 1}   -0.76{col 59}{space 3}0.445{col 67}{space 4} .9090061{col 80}{space 3} 1.042815
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .3945243{col 39}{space 2} .0405786{col 50}{space 1}   -9.04{col 59}{space 3}0.000{col 67}{space 4} .3224956{col 80}{space 3} .4826404
{txt}{space 13}p_government {c |}{col 27}{res}{space 2}  1.21842{col 39}{space 2} .2412989{col 50}{space 1}    1.00{col 59}{space 3}0.319{col 67}{space 4} .8264626{col 80}{space 3} 1.796268
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} 1.016465{col 39}{space 2} .0117446{col 50}{space 1}    1.41{col 59}{space 3}0.158{col 67}{space 4} .9937051{col 80}{space 3} 1.039747
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2}  1.02665{col 39}{space 2} .0102506{col 50}{space 1}    2.63{col 59}{space 3}0.008{col 67}{space 4} 1.006755{col 80}{space 3} 1.046939
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} 1.140853{col 39}{space 2} .0909225{col 50}{space 1}    1.65{col 59}{space 3}0.098{col 67}{space 4} .9758689{col 80}{space 3}  1.33373
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}  .078422{col 39}{space 2} .0304457{col 50}{space 1}   -6.56{col 59}{space 3}0.000{col 67}{space 4} .0366417{col 80}{space 3} .1678415
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .4328263{col 39}{space 2} .1604758{col 67}{space 4} .2092762{col 80}{space 3} .8951739
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen sample=e(sample)
{txt}
{com}. 
. melogit c_mainstream male age i.highedu i.income_3cat dissatisfied distpreviouspartycmp closeparty p_government sd_rile lvotetotniche_combined lpss_mod3_upd if p_niche==1 ///
> || country_elec:, or vce(robust)
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2261.6559}  
Iteration 1:{space 3}log likelihood = {res:-2256.0369}  
Iteration 2:{space 3}log likelihood = {res:-2256.0321}  
Iteration 3:{space 3}log likelihood = {res:-2256.0321}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2243.8894}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2243.8894}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-2237.3936}  
Iteration 2:{space 3}log pseudolikelihood = {res:  -2235.99}  
Iteration 3:{space 3}log pseudolikelihood = {res: -2235.372}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2235.3711}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2235.3711}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,515
{txt}Group variable: {res}country_elec{col 49}{txt}Number of groups{col 67}={res}{col 69}        32

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     141.1
{col 63}{txt}max{col 67}={res}{col 69}       420

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   227.54
{txt}Log pseudolikelihood = {res}-2235.3711{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 91:(Std. err. adjusted for {res:32} clusters in {res:country_elec})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}             c_mainstream{col 27}{c |} Odds ratio{col 39}   std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}male {c |}{col 27}{res}{space 2} .8236391{col 39}{space 2} .0882955{col 50}{space 1}   -1.81{col 59}{space 3}0.070{col 67}{space 4} .6675545{col 80}{space 3} 1.016219
{txt}{space 22}age {c |}{col 27}{res}{space 2} .9932294{col 39}{space 2} .0028128{col 50}{space 1}   -2.40{col 59}{space 3}0.016{col 67}{space 4} .9877317{col 80}{space 3} .9987577
{txt}{space 25} {c |}
{space 18}highedu {c |}
{space 5}Secondary education  {c |}{col 27}{res}{space 2} 1.298017{col 39}{space 2} .1592009{col 50}{space 1}    2.13{col 59}{space 3}0.033{col 67}{space 4}  1.02066{col 80}{space 3} 1.650743
{txt}Post-secondary education  {c |}{col 27}{res}{space 2} 1.039549{col 39}{space 2} .1456443{col 50}{space 1}    0.28{col 59}{space 3}0.782{col 67}{space 4} .7899304{col 80}{space 3} 1.368047
{txt}{space 25} {c |}
{space 14}income_3cat {c |}
{space 11}Medium income  {c |}{col 27}{res}{space 2} 1.125471{col 39}{space 2} .1155771{col 50}{space 1}    1.15{col 59}{space 3}0.250{col 67}{space 4}  .920285{col 80}{space 3} 1.376404
{txt}{space 13}High income  {c |}{col 27}{res}{space 2} 1.612894{col 39}{space 2} .1886577{col 50}{space 1}    4.09{col 59}{space 3}0.000{col 67}{space 4} 1.282455{col 80}{space 3} 2.028475
{txt}{space 25} {c |}
{space 13}dissatisfied {c |}{col 27}{res}{space 2} .7953482{col 39}{space 2} .0664123{col 50}{space 1}   -2.74{col 59}{space 3}0.006{col 67}{space 4} .6752758{col 80}{space 3}  .936771
{txt}{space 5}distpreviouspartycmp {c |}{col 27}{res}{space 2}   1.1182{col 39}{space 2} .0417348{col 50}{space 1}    2.99{col 59}{space 3}0.003{col 67}{space 4} 1.039322{col 80}{space 3} 1.203065
{txt}{space 15}closeparty {c |}{col 27}{res}{space 2} .4279681{col 39}{space 2} .0407272{col 50}{space 1}   -8.92{col 59}{space 3}0.000{col 67}{space 4} .3551466{col 80}{space 3} .5157214
{txt}{space 13}p_government {c |}{col 27}{res}{space 2} .7525457{col 39}{space 2} .1463434{col 50}{space 1}   -1.46{col 59}{space 3}0.144{col 67}{space 4}  .514049{col 80}{space 3} 1.101695
{txt}{space 18}sd_rile {c |}{col 27}{res}{space 2} .9915717{col 39}{space 2}  .011749{col 50}{space 1}   -0.71{col 59}{space 3}0.475{col 67}{space 4} .9688094{col 80}{space 3} 1.014869
{txt}{space 3}lvotetotniche_combined {c |}{col 27}{res}{space 2} .9944645{col 39}{space 2} .0079455{col 50}{space 1}   -0.69{col 59}{space 3}0.487{col 67}{space 4}  .979013{col 80}{space 3}  1.01016
{txt}{space 12}lpss_mod3_upd {c |}{col 27}{res}{space 2} .9129143{col 39}{space 2} .0428029{col 50}{space 1}   -1.94{col 59}{space 3}0.052{col 67}{space 4} .8327614{col 80}{space 3} 1.000782
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}    .5944{col 39}{space 2} .2494334{col 50}{space 1}   -1.24{col 59}{space 3}0.215{col 67}{space 4} .2611449{col 80}{space 3} 1.352932
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}country_elec             {col 27}{txt}{c |}
{space 16}var(_cons){c |}{col 27}{res}{space 2} .1920232{col 39}{space 2} .1404231{col 67}{space 4} .0458021{col 80}{space 3} .8050484
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {help eform_option:Estimates are transformed} only in the first equation to odds ratios.{p_end}
{p 0 6 2}Note: {bf:_cons} estimates baseline odds (conditional on zero random effects){txt}.{p_end}

{com}. gen sample2=e(sample)
{txt}
{com}. 
. gen inanalysis=1 if sample==1 | sample2==1
{txt}(202,374 missing values generated)

{com}. 
. keep if inanalysis==1
{txt}(202,374 observations deleted)

{com}. keep resid lpss_mod3_upd party_a-party_i Aniche_max- Iniche_max Aradical_max-Iradical_max country elec_year male age highedu income disat_demo pol_info closeparty
{txt}
{com}. 
. rename party_a party1
{res}{txt}
{com}. rename party_b party2
{res}{txt}
{com}. rename party_c party3
{res}{txt}
{com}. rename party_d party4
{res}{txt}
{com}. rename party_e party5
{res}{txt}
{com}. rename party_f party6
{res}{txt}
{com}. rename party_g party7
{res}{txt}
{com}. rename party_h party8
{res}{txt}
{com}. rename party_i party9
{res}{txt}
{com}. 
. rename Aradical_max radical1
{res}{txt}
{com}. rename Bradical_max radical2
{res}{txt}
{com}. rename Cradical_max radical3
{res}{txt}
{com}. rename Dradical_max radical4
{res}{txt}
{com}. rename Eradical_max radical5
{res}{txt}
{com}. rename Fradical_max radical6
{res}{txt}
{com}. rename Gradical_max radical7
{res}{txt}
{com}. rename Hradical_max radical8
{res}{txt}
{com}. rename Iradical_max radical9
{res}{txt}
{com}. reshape long party radical, i(resid)
{txt}(j = 1 2 3 4 5 6 7 8 9)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}      30,387   {txt}->   {res}273,483     
{txt}Number of variables        {res}          38   {txt}->   {res}23          
{txt}j variable (9 values)                     ->   {res}_j
{txt}xij variables:
               {res}party1 party2 ... party9   {txt}->   {res}party
         radical1 radical2 ... radical9   {txt}->   {res}radical
{txt}{hline 77}

{com}. drop if party==.
{txt}(91,249 observations deleted)

{com}. 
. bys resid: egen zpos=std(party)
{txt}(1,359 missing values generated)

{com}. replace zpos=(zpos*-1) if zpos<0
{txt}(88,168 real changes made)

{com}. 
. reg zpos i.radical##c.lpss_mod3_upd male age highedu pol_info closeparty

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}   109,357
{txt}{hline 13}{c +}{hline 34}   F(8, 109348)    = {res}  2965.99
{txt}       Model {c |} {res} 4580.36022         8  572.545028   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 21108.1738   109,348   .19303667   {txt}R-squared       ={res}    0.1783
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1782
{txt}       Total {c |} {res}  25688.534   109,356  .234907404   {txt}Root MSE        =   {res} .43936

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   zpos{col 25}{c |} Coefficient{col 37}  Std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.radical {c |}{col 25}{res}{space 2} .4661724{col 37}{space 2} .0031291{col 48}{space 1}  148.98{col 57}{space 3}0.000{col 65}{space 4} .4600394{col 78}{space 3} .4723055
{txt}{space 10}lpss_mod3_upd {c |}{col 25}{res}{space 2}-.0245494{col 37}{space 2} .0012705{col 48}{space 1}  -19.32{col 57}{space 3}0.000{col 65}{space 4}-.0270395{col 78}{space 3}-.0220593
{txt}{space 23} {c |}
radical#c.lpss_mod3_upd {c |}
{space 21}1  {c |}{col 25}{res}{space 2} .0556328{col 37}{space 2} .0036312{col 48}{space 1}   15.32{col 57}{space 3}0.000{col 65}{space 4} .0485157{col 78}{space 3} .0627499
{txt}{space 23} {c |}
{space 19}male {c |}{col 25}{res}{space 2}-.0073819{col 37}{space 2} .0026655{col 48}{space 1}   -2.77{col 57}{space 3}0.006{col 65}{space 4}-.0126062{col 78}{space 3}-.0021575
{txt}{space 20}age {c |}{col 25}{res}{space 2} .0006551{col 37}{space 2} .0000898{col 48}{space 1}    7.30{col 57}{space 3}0.000{col 65}{space 4} .0004792{col 78}{space 3}  .000831
{txt}{space 16}highedu {c |}{col 25}{res}{space 2} .0063305{col 37}{space 2} .0020685{col 48}{space 1}    3.06{col 57}{space 3}0.002{col 65}{space 4} .0022763{col 78}{space 3} .0103848
{txt}{space 15}pol_info {c |}{col 25}{res}{space 2} .0017596{col 37}{space 2} .0013422{col 48}{space 1}    1.31{col 57}{space 3}0.190{col 65}{space 4} -.000871{col 78}{space 3} .0043903
{txt}{space 13}closeparty {c |}{col 25}{res}{space 2}  .010363{col 37}{space 2}  .002693{col 48}{space 1}    3.85{col 57}{space 3}0.000{col 65}{space 4} .0050847{col 78}{space 3} .0156413
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} .6196611{col 37}{space 2} .0077948{col 48}{space 1}   79.50{col 57}{space 3}0.000{col 65}{space 4} .6043835{col 78}{space 3} .6349387
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(lpss_mod3_upd=(-4(0.5)2.5) radical=(1)) vsquish post
{res}
{txt}{col 1}Predictive margins{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:109,357}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-4}}
{lalign 8:2._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3.5}}
{lalign 8:3._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3}}
{lalign 8:4._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2.5}}
{lalign 8:5._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2}}
{lalign 8:6._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1.5}}
{lalign 8:7._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1}}
{lalign 8:8._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-.5}}
{lalign 8:9._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:0}}
{lalign 8:10._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:.5}}
{lalign 8:11._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1}}
{lalign 8:12._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1.5}}
{lalign 8:13._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2}}
{lalign 8:14._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:1}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2.5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 1.014039{col 26}{space 2} .0144742{col 37}{space 1}   70.06{col 46}{space 3}0.000{col 54}{space 4} .9856699{col 67}{space 3} 1.042408
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1.029581{col 26}{space 2} .0128019{col 37}{space 1}   80.42{col 46}{space 3}0.000{col 54}{space 4} 1.004489{col 67}{space 3} 1.054672
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.045122{col 26}{space 2} .0111394{col 37}{space 1}   93.82{col 46}{space 3}0.000{col 54}{space 4} 1.023289{col 67}{space 3} 1.066955
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.060664{col 26}{space 2} .0094919{col 37}{space 1}  111.74{col 46}{space 3}0.000{col 54}{space 4}  1.04206{col 67}{space 3} 1.079268
{txt}{space 10}5  {c |}{col 14}{res}{space 2} 1.076206{col 26}{space 2} .0078687{col 37}{space 1}  136.77{col 46}{space 3}0.000{col 54}{space 4} 1.060783{col 67}{space 3} 1.091628
{txt}{space 10}6  {c |}{col 14}{res}{space 2} 1.091747{col 26}{space 2} .0062886{col 37}{space 1}  173.61{col 46}{space 3}0.000{col 54}{space 4} 1.079422{col 67}{space 3} 1.104073
{txt}{space 10}7  {c |}{col 14}{res}{space 2} 1.107289{col 26}{space 2} .0047947{col 37}{space 1}  230.94{col 46}{space 3}0.000{col 54}{space 4} 1.097892{col 67}{space 3} 1.116687
{txt}{space 10}8  {c |}{col 14}{res}{space 2} 1.122831{col 26}{space 2}  .003499{col 37}{space 1}  320.90{col 46}{space 3}0.000{col 54}{space 4} 1.115973{col 67}{space 3} 1.129689
{txt}{space 10}9  {c |}{col 14}{res}{space 2} 1.138373{col 26}{space 2} .0027032{col 37}{space 1}  421.12{col 46}{space 3}0.000{col 54}{space 4} 1.133074{col 67}{space 3} 1.143671
{txt}{space 9}10  {c |}{col 14}{res}{space 2} 1.153914{col 26}{space 2} .0028606{col 37}{space 1}  403.38{col 46}{space 3}0.000{col 54}{space 4} 1.148308{col 67}{space 3} 1.159521
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 1.169456{col 26}{space 2} .0038561{col 37}{space 1}  303.27{col 46}{space 3}0.000{col 54}{space 4} 1.161898{col 67}{space 3} 1.177014
{txt}{space 9}12  {c |}{col 14}{res}{space 2} 1.184998{col 26}{space 2} .0052314{col 37}{space 1}  226.52{col 46}{space 3}0.000{col 54}{space 4} 1.174744{col 67}{space 3} 1.195251
{txt}{space 9}13  {c |}{col 14}{res}{space 2} 1.200539{col 26}{space 2} .0067584{col 37}{space 1}  177.64{col 46}{space 3}0.000{col 54}{space 4} 1.187293{col 67}{space 3} 1.213786
{txt}{space 9}14  {c |}{col 14}{res}{space 2} 1.216081{col 26}{space 2} .0083544{col 37}{space 1}  145.56{col 46}{space 3}0.000{col 54}{space 4} 1.199707{col 67}{space 3} 1.232456
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix y_=r(table)'
{txt}
{com}. svmat y_
{txt}
{com}. range g -4 2.5 14
{txt}(182,220 missing values generated)

{com}. 
. foreach v of varlist y_1-y_9 {c -(}
{txt}  2{com}.         rename `v' `v'_radical  
{txt}  3{com}. {c )-}
{res}{txt}
{com}. 
. reg zpos i.radical##c.lpss_mod3_upd male age highedu pol_info closeparty

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}   109,357
{txt}{hline 13}{c +}{hline 34}   F(8, 109348)    = {res}  2965.99
{txt}       Model {c |} {res} 4580.36022         8  572.545028   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 21108.1738   109,348   .19303667   {txt}R-squared       ={res}    0.1783
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1782
{txt}       Total {c |} {res}  25688.534   109,356  .234907404   {txt}Root MSE        =   {res} .43936

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   zpos{col 25}{c |} Coefficient{col 37}  Std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.radical {c |}{col 25}{res}{space 2} .4661724{col 37}{space 2} .0031291{col 48}{space 1}  148.98{col 57}{space 3}0.000{col 65}{space 4} .4600394{col 78}{space 3} .4723055
{txt}{space 10}lpss_mod3_upd {c |}{col 25}{res}{space 2}-.0245494{col 37}{space 2} .0012705{col 48}{space 1}  -19.32{col 57}{space 3}0.000{col 65}{space 4}-.0270395{col 78}{space 3}-.0220593
{txt}{space 23} {c |}
radical#c.lpss_mod3_upd {c |}
{space 21}1  {c |}{col 25}{res}{space 2} .0556328{col 37}{space 2} .0036312{col 48}{space 1}   15.32{col 57}{space 3}0.000{col 65}{space 4} .0485157{col 78}{space 3} .0627499
{txt}{space 23} {c |}
{space 19}male {c |}{col 25}{res}{space 2}-.0073819{col 37}{space 2} .0026655{col 48}{space 1}   -2.77{col 57}{space 3}0.006{col 65}{space 4}-.0126062{col 78}{space 3}-.0021575
{txt}{space 20}age {c |}{col 25}{res}{space 2} .0006551{col 37}{space 2} .0000898{col 48}{space 1}    7.30{col 57}{space 3}0.000{col 65}{space 4} .0004792{col 78}{space 3}  .000831
{txt}{space 16}highedu {c |}{col 25}{res}{space 2} .0063305{col 37}{space 2} .0020685{col 48}{space 1}    3.06{col 57}{space 3}0.002{col 65}{space 4} .0022763{col 78}{space 3} .0103848
{txt}{space 15}pol_info {c |}{col 25}{res}{space 2} .0017596{col 37}{space 2} .0013422{col 48}{space 1}    1.31{col 57}{space 3}0.190{col 65}{space 4} -.000871{col 78}{space 3} .0043903
{txt}{space 13}closeparty {c |}{col 25}{res}{space 2}  .010363{col 37}{space 2}  .002693{col 48}{space 1}    3.85{col 57}{space 3}0.000{col 65}{space 4} .0050847{col 78}{space 3} .0156413
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} .6196611{col 37}{space 2} .0077948{col 48}{space 1}   79.50{col 57}{space 3}0.000{col 65}{space 4} .6043835{col 78}{space 3} .6349387
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, at(lpss_mod3_upd=(-4(0.5)2.5) radical=(0)) vsquish post
{res}
{txt}{col 1}Predictive margins{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:109,357}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-4}}
{lalign 8:2._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3.5}}
{lalign 8:3._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-3}}
{lalign 8:4._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2.5}}
{lalign 8:5._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-2}}
{lalign 8:6._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1.5}}
{lalign 8:7._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-1}}
{lalign 8:8._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:-.5}}
{lalign 8:9._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:0}}
{lalign 8:10._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:.5}}
{lalign 8:11._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1}}
{lalign 8:12._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:1.5}}
{lalign 8:13._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2}}
{lalign 8:14._at: }{space 0}{lalign 13:radical} = {res:{ralign 4:0}}
{lalign 8:}{space 0}{lalign 13:lpss_mod3_upd} = {res:{ralign 4:2.5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .7703976{col 26}{space 2} .0049845{col 37}{space 1}  154.56{col 46}{space 3}0.000{col 54}{space 4}  .760628{col 67}{space 3} .7801672
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .7581229{col 26}{space 2} .0043847{col 37}{space 1}  172.90{col 46}{space 3}0.000{col 54}{space 4}  .749529{col 67}{space 3} .7667168
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .7458482{col 26}{space 2} .0037964{col 37}{space 1}  196.46{col 46}{space 3}0.000{col 54}{space 4} .7384074{col 67}{space 3}  .753289
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .7335735{col 26}{space 2} .0032259{col 37}{space 1}  227.40{col 46}{space 3}0.000{col 54}{space 4} .7272508{col 67}{space 3} .7398962
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .7212989{col 26}{space 2} .0026847{col 37}{space 1}  268.67{col 46}{space 3}0.000{col 54}{space 4} .7160369{col 67}{space 3} .7265608
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .7090242{col 26}{space 2} .0021945{col 37}{space 1}  323.10{col 46}{space 3}0.000{col 54}{space 4} .7047231{col 67}{space 3} .7133253
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .6967495{col 26}{space 2} .0017975{col 37}{space 1}  387.63{col 46}{space 3}0.000{col 54}{space 4} .6932265{col 67}{space 3} .7002725
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .6844748{col 26}{space 2} .0015662{col 37}{space 1}  437.02{col 46}{space 3}0.000{col 54}{space 4}  .681405{col 67}{space 3} .6875446
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .6722001{col 26}{space 2} .0015756{col 37}{space 1}  426.64{col 46}{space 3}0.000{col 54}{space 4}  .669112{col 67}{space 3} .6752883
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .6599255{col 26}{space 2} .0018218{col 37}{space 1}  362.25{col 46}{space 3}0.000{col 54}{space 4} .6563548{col 67}{space 3} .6634961
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .6476508{col 26}{space 2} .0022276{col 37}{space 1}  290.74{col 46}{space 3}0.000{col 54}{space 4} .6432847{col 67}{space 3} .6520169
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .6353761{col 26}{space 2} .0027226{col 37}{space 1}  233.37{col 46}{space 3}0.000{col 54}{space 4} .6300398{col 67}{space 3} .6407124
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .6231014{col 26}{space 2} .0032665{col 37}{space 1}  190.75{col 46}{space 3}0.000{col 54}{space 4} .6166991{col 67}{space 3} .6295038
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .6108267{col 26}{space 2} .0038386{col 37}{space 1}  159.13{col 46}{space 3}0.000{col 54}{space 4} .6033032{col 67}{space 3} .6183503
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix y_=r(table)'
{txt}
{com}. svmat y_
{txt}
{com}. 
. twoway hist lpss_mod3_upd, percent yaxis(2) ///
> || line y_5_radical g, lcolor(black) lpattern(dash dash) ///
> || line y_6_radical g, lcolor(black) lpattern(dash dash) ///
> || line y_1_radical g, lcolor(black) lpattern(solid) ///
> || line y_5 g, lcolor(gs10) lpattern(dash dash) ///
> || line y_6 g, lcolor(gs10) lpattern(dash dash) ///
> || line y_1 g, lcolor(gs10) lpattern(solid) ///
> , xlabel(-4(0.5)2.5) xtitle("Party system saturation t-1", size(medsmall)) ///
> yscale(alt) yscale(alt axis(2)) ///
> ytitle("% of observations", size(medsmall) axis(2)) ///
> ytitle("Z-score perceived party position", size(medsmall) axis(1)) ///
> legend(order(4 "Radical party" 7 "Mainstream party") ring(0) position(11)) ///
> scheme(plotplain) name(b, replace)
{res}{txt}
{com}. 
. restore
{txt}
{com}. 
. graph combine a b, scheme(plotplain) 
{res}{txt}
{com}. graph export "figure5.tif", replace
{txt}{p 0 4 2}
file {bf}
figure5.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\LT-054912\surfdrive\Voting for niche parties\dataverse files\Stata commands to replicate all analyses.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 3 Oct 2023, 17:58:32
{txt}{.-}
{smcl}
{txt}{sf}{ul off}